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Zhang, Hong, Dechu Ge, and Siyu Zhang. "Hybrid recommendation system based on semantic interest community and trusted neighbors." Multimedia Tools and Applications 77, no. 4 (March 20, 2017): 4187–202. http://dx.doi.org/10.1007/s11042-017-4553-9.
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Zheng, Jianxing, Suge Wang, Deyu Li, and Bofeng Zhang. "Personalized recommendation based on hierarchical interest overlapping community." Information Sciences 479 (April 2019): 55–75. http://dx.doi.org/10.1016/j.ins.2018.11.054.
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Zheng, Jianxing, and Yanjie Wang. "Personalized Recommendations Based on Sentimental Interest Community Detection." Scientific Programming 2018 (August 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/8503452.
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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users’ implicit interests. Sentiments on topics imply users’ sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.
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Wenwen, Zhou. "Building an Urban Smart Community System Based on Association Rule Algorithms." Security and Communication Networks 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/8773259.
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Intelligent system development is an integral component of smart community development and has a significant impact on the development of smart communities. Some cities continue to implement personalized smart community services, resulting in the formation of smart city communities with unique characteristics. Urban smart communities are based on the principle of owner-occupant convenience, integrating a wealth of community information and making it more relevant to each and every resident through intelligent management. Increasing information transmission rates have enhanced the ability of smart community systems to integrate information, but the smart community recommendation method is still based on traditional categorized recommendations. This paper addresses the deficiency of recommended information in smart urban communities. By analyzing user interaction and operation data, we can determine the interest and recognition of browsing attractions among users. Compared to conventional classification recommendations, weighted association rules can identify potentially very important rules applicable to small groups, thereby meeting the needs of various groups and enabling personalized services. Through continuous feedback from user behavior data, the system gradually identifies the community information that users are interested in during the specific recommendation process. After testing, the smart community system’s recommendation accuracy and real-time performance have vastly improved in comparison to categorical recommendations, and it can effectively meet the needs of tenants for community recommendations.
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Zhou, Tom, Hao Ma, Michael Lyu, and Irwin King. "UserRec: A User Recommendation Framework in Social Tagging Systems." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1486–91. http://dx.doi.org/10.1609/aaai.v24i1.7524.
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Social tagging systems have emerged as an effective way for users to annotate and share objects on the Web. However, with the growth of social tagging systems, users are easily overwhelmed by the large amount of data and it is very difficult for users to dig out information that he/she is interested in. Though the tagging system has provided interest-based social network features to enable the user to keep track of other users' tagging activities, there is still no automatic and effective way for the user to discover other users with common interests. In this paper, we propose a User Recommendation (UserRec) framework for user interest modeling and interest-based user recommendation, aiming to boost information sharing among users with similar interests. Our work brings three major contributions to the research community: (1) we propose a tag-graph based community detection method to model the users' personal interests, which are further represented by discrete topic distributions; (2) the similarity values between users' topic distributions are measured by Kullback-Leibler divergence (KL-divergence), and the similarity values are further used to perform interest-based user recommendation; and (3) by analyzing users' roles in a tagging system, we find users' roles in a tagging system are similar to Web pages in the Internet. Experiments on tagging dataset of Web pages (Yahoo!~Delicious) show that UserRec outperforms other state-of-the-art recommender system approaches.
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Gan, Mingxin, and Xiongtao Zhang. "Integrating Community Interest and Neighbor Semantic for Microblog Recommendation." International Journal of Web Services Research 18, no. 2 (April 2021): 54–75. http://dx.doi.org/10.4018/ijwsr.2021040104.
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As a typical characteristic of microblog information, short text length makes a microblog recommendation hard for new users. Moreover, user cold start makes it difficult to explore accurately the interests of microblog users. Therefore, the authors proposed a microblog recommendation model that integrates both of the users' interest from their communities and the semantic from their neighbors' microblogs. Based on the Kullback-Leibler (KL) language model, the proposed model estimated an interest-based language model and a microblog-based language model. Specifically, the interest-based language model was estimated based on both of the user's word set of interest and that of their community interest. Meanwhile, the microblog-based language model was estimated by combining the word set of a microblog, the neighbor semantic, and the microblog set. Real data from Sina Weibo was crawled to evaluate recommendation performance. Results showed that the proposed model outperforms state-of-art models significantly.
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Tang, Lei, Dandan Cai, Zongtao Duan, Junchi Ma, Meng Han, and Hanbo Wang. "Discovering Travel Community for POI Recommendation on Location-Based Social Networks." Complexity 2019 (February 12, 2019): 1–8. http://dx.doi.org/10.1155/2019/8503962.
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Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.
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Shokrzadeh, Zeinab, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, and Jamshid Bagherzadeh Mohasefi. "Graph-Based Recommendation System Enhanced by Community Detection." Scientific Programming 2023 (August 21, 2023): 1–12. http://dx.doi.org/10.1155/2023/5073769.
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Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.
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Kumar, Akshi, and Saurabh Raj Sangwan. "Expert Finding in Community Question-Answering for Post Recommendation." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.4.16764.
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Community question answering system is a perfect example of platform where people participate to seek expertise on their topic of interest. But information overload, finding the expertise level of users and trustworthy answers remain key challenges within these communities. Moreover, people do not look for personal advices but expert views on such platforms therefore; expert finding is an integral part of these communities. In order to trust someone's opinion who is not known in person by the users of the community, it is necessary to find the credibility of such person. By determining expertise levels of users, authenticity of their posts can easily be determined. Also, by identifying experts, each expert will be shown relevant posts to indulge in so that he can use his knowledge and skills to give valid and correct answers. For users too, it will be easy to find reliable answers, once they get to know the expertise level of the answerers. Motivated by these facts, we put forward a framework for finding experts in online question answer community (stackoverflow) referred to as Expert Recommender System which uses a well-recognized global-trust metric, PageRankTM for finding experts in the community building a Trust-based system and then uses collaborative filtering to find similar experts based on their level of expertise and their topics of interests to a particular user. Once we have the top- k similar experts to a given expert, that expert is recommended with posts to collaborate upon, based on activities done by his top-k neighbor experts. The framework is evaluated for its performance and it clearly indicates the effectiveness of the system.
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Liu, Jing, and Yong Zhong. "Time-Weighted Community Search Based on Interest." Applied Sciences 12, no. 14 (July 13, 2022): 7077. http://dx.doi.org/10.3390/app12147077.
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Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to select users’ representative attributes in combination with time. Therefore, we propose a time-weighted community search model (TWC) based on user interests which fully considers the impact of time on user interests. TWC reduces the number of query parameters as much as possible and improves the usability of the model. We design the time-weighted decay function of the attribute. We then extract the user’s time-weighted representative attributes to express the user’s short-term interests more clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. To solve the TWC problem, we design and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the proposed algorithm.
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Gede Dwidasmara, Ida Bagus, I. Gusti Ngurah Agung Widiaksa Putra, I. Made Widiartha, I. Wayan Santiyasa, Ida Bagus Made Mahendra, and Anak Agung Istri Ngurah Eka Karyawati. "SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 10, no. 2 (January 4, 2022): 227. http://dx.doi.org/10.24843/jlk.2021.v10.i02.p05.
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Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions.
Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.
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Anandhan, Anitha, Maizatul Akmar Ismail, and Liyana Shuib. "EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING." Malaysian Journal of Computer Science 35, no. 3 (July 27, 2022): 201–21. http://dx.doi.org/10.22452/mjcs.vol35no3.2.
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Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
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Adraoui, Meriem, Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, and Samir Bennani. "Towards a hybrid recommendation approach using a community detection and evaluation algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6718. http://dx.doi.org/10.11591/ijece.v13i6.pp6718-6728.
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<span lang="EN-US">In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy.</span>
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Yuan-mei, WANG, WANG Ya-jun, and ZHAO Shi-bo. "Personalized Explainable Recommendation based on BERT." March 2023 5, no. 1 (March 24, 2023): 24–38. http://dx.doi.org/10.36548/jaicn.2023.1.003.
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With the proliferation of text information on the Internet, the rapid evolution of artificial intelligence, and the wide application of machine learning and deep learning, text emotion analysis has been widely concerned by the academic community. Personalized recommendation system has emerged as the circumstances demanded, and has quickly gained interest in both industry and academic circles. It has gradually become an extremely important part of people's life and work in many fields, such as e-commerce, short video content, take-out service, online advertising push and so on. First, the Chinese comment text of an item is analyzed in this paper. Since the absolute standard of binary text sentiment analysis cannot meet the need of the recommendation system to recommend the item to users for interpretation, a ternary text sentiment analysis method based on BERT model is used to combine with the idiosyncrasies of text data is proposed to solve the problems of poor Chinese text representation, low exactitude, and inability to precisely comprehend the semantic information expressed in the text, which are caused by polysemy of Chinese version. The proposed method can generate interpretable recommendations for items that users are interested in. The relevant properties of the text are captured by the Transformer encoder in the BERT model, meanwhile the attention framework is used to weight the information recovered from the pattern to highlight the hinge information in the comment on the text. Second, the SoftMax function is used for categorizing the text aspect data of users' reviews of items, as well as finally the recommendation system recommends interested items to users and produces emotional and coloration reasons for recommendation that are accorded with users' justifications. The method is applied to real datasets, and the results show that text breakdown effect has been achieved, which greatly improves the interpretability of recommendation system, which is more in line with users' ideas.
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Zheng, Fangxia. "Personalized Education Based on Hybrid Intelligent Recommendation System." Journal of Mathematics 2022 (January 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/1313711.
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Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.
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Zhang, Pei Ying, Ya Jun Du, and Chang Wang. "Clustering Users According to Common Interest Based on User Search Behavior." Advanced Materials Research 143-144 (October 2010): 851–55. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.851.
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The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.
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Kabutoya, Yutaka, Tomoharu Iwata, Hisako Shiohara, and Ko Fujimura. "Effective Question Recommendation Based on Multiple Features for Question Answering Communities." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 259–62. http://dx.doi.org/10.1609/icwsm.v4i1.14042.
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We propose a new method of recommending questions to answerers so as to suit the answerers’ knowledge and interests in User-Interactive Question Answering (QA) communities. A question recommender can help answerers select the questions that interest them. This increases the number of answers, which will activate QA communities. An effective question recommender should satisfy the following three requirements: First, its accuracy should be higher than the existing category-based approach; more than 50% of answerers select the questions to answer according a fixed system of categories. Second, it should be able to recommend unanswered questions because more than 2,000 questions are posted every day. Third, it should be able to support even those people who have never answered a question previously, because more than 50% of users in current QA communities have never given any answer. To achieve an effective question recommender, we use question histories as well as the answer histories of each user by combining collaborative filtering schemes and content-base filtering schemes. Experiments on real log data sets of a famous Japanese QA community, Oshiete goo, show that our recommender satisfies the three requirements.
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Liu, Jingtong, Huawei Yi, Yixuan Gao, and Rong Jing. "Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network." Electronics 12, no. 16 (August 18, 2023): 3495. http://dx.doi.org/10.3390/electronics12163495.
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Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.
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Olshannikova, Ekaterina, Erjon Skenderi, Thomas Olsson, Sami Koivunen, and Jukka Huhtamäki. "Utilizing Structural Network Positions to Diversify People Recommendations on Twitter." Advances in Human-Computer Interaction 2022 (January 30, 2022): 1–17. http://dx.doi.org/10.1155/2022/6584394.
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Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.
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Marquardt, Roberto, Juris Meija, Zoltán Mester, Marcy Towns, Ron Weir, Richard Davis, and Jürgen Stohner. "Definition of the mole (IUPAC Recommendation 2017)." Pure and Applied Chemistry 90, no. 1 (January 26, 2018): 175–80. http://dx.doi.org/10.1515/pac-2017-0106.
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AbstractIn 2011 the General Conference on Weights and Measures (CGPM) noted the intention of the International Committee for Weights and Measures (CIPM) to revise the entire International System of Units (SI) by linking all seven base units to seven fundamental physical constants. Of particular interest to chemists, new definitions for the kilogram and the mole have been proposed. A recent IUPAC Technical Report discussed these new definitions in relation to immediate consequences for the chemical community. This IUPAC Recommendation on the preferred definition of the mole follows from this Technical Report. It supports a definition of the mole based on a specified number of elementary entities, in contrast to the present 1971 definition.
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Shahbazi, Zeinab, Debapriya Hazra, Sejoon Park, and Yung Cheol Byun. "Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches." Symmetry 12, no. 9 (September 22, 2020): 1566. http://dx.doi.org/10.3390/sym12091566.
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With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.
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Ge, Jun, Lei-lei Shi, Lu Liu, Hongwei Shi, and John Panneerselvam. "Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks." Wireless Communications and Mobile Computing 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/3860083.
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Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.
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Suhandi, Nazori, and Rendra Gustriansyah. "The Housing Recommendation System Uses Multi-Criteria Decision-Making Methods." Journal of Computer Networks, Architecture and High Performance Computing 5, no. 2 (July 20, 2023): 552–62. http://dx.doi.org/10.47709/cnahpc.v5i2.2497.
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Economic and population growth, increasing urbanization, changing habits, new welfare requirements, and lower interest rates have led to increased demand for housing in cities. However, housing conditions in many cities are slightly alarming, while housing is a primary need for the community. Selecting housing for low-income people (LIP) that meets the criteria required by LIP is not an easy task. Because most of the decisions people made did not utilize detailed information. Therefore, a recommendation system for LIP is required. This study aims to develop the housing selection recommendation system for LIP that best suits their wishes. This study integrated two multi-criteria decision-making (MCDM) methods: the Best Worst (BW) method, which has fewer pairwise comparisons compared to other MCDM methods for selecting criteria and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for determining housing recommendations for LIP according to their wishes. Based on the analysis results, ten criteria dominate the housing selection for LIP sequentially: Location, Land Size, Down Payment, Public Facilities, Price, Booking Fee, Home Design, House Specifications, House Quality, and Home Ownership Credit. Furthermore, the sensitivity analysis results showed that the robustness score of this approach was high. The model could recommend housing for LIP that best suits their wishes.
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Xu, Chunyan. "A Personalized Recommender System Based on Library Database." International Journal of Emerging Technologies in Learning (iJET) 12, no. 12 (December 20, 2017): 134. http://dx.doi.org/10.3991/ijet.v12i12.7964.
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In recent years, the university libraries in China have acquired increasingly abundant electronic resources. However, the information silo phenomenon appears due to the lack of connection between university IT system and the community. Based on the book borrowing, favourite collection, comments and social relationship of students, this paper digs into the personalized interests of students, and promotes the design and implementation of a personalized recommender system. Specifically, the overall framework and recommender engine of the system were created based on the library data services. The modules in the system were also elaborated, and the recommendation results were verified by an offline test.
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Lee, Danielle, and Peter Brusilovsky. "Recommending Talks at Research Conferences Using Users' Social Networks." International Journal of Cooperative Information Systems 23, no. 02 (June 2014): 1441003. http://dx.doi.org/10.1142/s0218843014410032.
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This paper investigates recommendation algorithms to suggest talks of interest to attendees of research conferences. In this study, based on a social conference support system Conference Navigator 3 (CN3), we explored three kinds of knowledge sources to generate recommendations: users' preference about talks (CN3 bookmarks), users' social networks (research collaboration network and CN3 following network) and talk content information (titles and abstracts). Using these sources, we explored a diverse set of algorithms from non-personalized community vote-based recommendations and conventional collaborative filtering recommendations to hybrid recommendations such as social network-based (SN) recommendations boosted by content information of talks. We found that SN recommendations fused with content information outperformed the other approaches. Moreover, for cold-start users who have an insufficient number of bookmarks to express their preferences, the recommendations based on their social connections also generated significantly better suggestions than the other approaches. Between two kinds of social networks that we considered as foundations of recommendations, there was no significant difference in the quality of the recommendations.
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Ben Zaken, Daniel, Kobi Gal, Guy Shani, Avi Segal, and Darlene Cavalier. "Intelligent Recommendations for Citizen Science." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14693–701. http://dx.doi.org/10.1609/aaai.v35i17.17726.
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Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users' interactions in the SciStarter platform - a leading citizen science site -as well as their contributions to different projects. The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using non-personalized recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.
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Kusnadi, Adhi, and Edwin Kurniawan. "Implementation of Topsis Method In Web Based System Recommendations For Students Laptop Selection (Case Study: Bhinneka.com)." International Journal of New Media Technology 4, no. 1 (June 16, 2017): 42–45. http://dx.doi.org/10.31937/ijnmt.v4i1.537.
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Computer needs at work are very helpful and make easier for human to complete his work. The usageof laptops growing rapidly with increasing mobility of the community who uses laptop to run the activities. In addition, laptop continues to update its technology with the variation of the specification so that it can attract the interest of consumers, especially for students, but the variety of laptops is confusing for some users to choose. Based on a survey that has been done to the students, it concluded that laptop is 80% more interesting than desktop computer. Some students really need a recommendation system for choosing alaptop. In making a recommendation system, using TOPSIS method is the recommended one because the concept is simple, easy to understand, efficient, and have the ability to measure the relative performance of alternatives decision. According to the implementation, the recommendation system with TOPSIS method has 70% accuracy rate.
Index Terms—System Recommendation, TOPSIS, Laptop,
REFERENCES
[1] Hendra, dkk. 2007. “Keluhan Kesehatan pada Penggunaan Laptop pada Mahasiswa FKM UI”. (pdf). Depok: Universitas Indonesia.
[2] Paays, N. 2013. Rancang Bangun Meja Laptop Yang Adjustable Berdasarkan Aspek Ergonomi. Pontianak: Universitas Tanjungpura.
[3] Iqbal, M. 2014. Perilaku Pembelian Laptop Oleh Mahasiswa Strata 1 Universitas Brawijaya Malang. S1 thesis. Malang: Universitas Brawijaya.
[4] Setiawan, H. 2014. Rancang Bangun Aplikasi Rekomendasi Pembelian Laptop Dengan Metode Fuzzy Database Tahani Model Berbasis Web (Studi Kasus: Toko Ricky Komputer). S1 thesis. Tangerang: Universitas Multimedia Nusantara.
[5] Fitriani, S. A. 2014. Perbandingan Metode Weighted Product dengan Metode Technique for Order Preference by Similarity to Ideal Solution dalam Pendukung Keputusan Perekruten Siswa/Mahasiswa Praktek Kerja Lapangan. S1 thesis. Bandung : Universitas Pendidikan Indonesia.
[6] Buaton, R.2014. 15 Metode Menyelesaikan Data Mining, Sistem Pakar, dan Sistem Pendukung Keputusan (.pdf). Medan.
[7] Salehah, A. 2014. Penerapan TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) Untuk Seleksi Penerimaan Beasiswa (Studi Kasus Pendaftar Beasiswa dari Fakultas MIPA di Universitas Brawijaya). S1 thesis. Malang : Universitas Brawijaya.
[8] Gay, L.R. dan Diehl, P.L. (1992), Research Methods for Business and. Management, MacMillan Publishing Company, New York
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Homann, Leschek, Denis Mayr Lima Martins, Gottfried Vossen, and Karsten Kraume. "Enhancing Traditional Recommender Systems via Social Communities." Vietnam Journal of Computer Science 06, no. 01 (February 2019): 3–16. http://dx.doi.org/10.1142/s2196888819500040.
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Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no previous preference information is available) and the gray sheep problem (i.e., cases in which a user profile does not match any other profile in the user community) are widely recognized for hindering recommendation effectiveness of traditional CF methods. To alleviate such problems, substantial research has focused on enhancing CF with social information about users (e.g., social relationships and communities). However, despite the crescent interest in social-based approaches, researches and practitioners face the challenge of developing their own Recommender System architecture for appropriately combining social and collaborative filtering methods to improve recommendation results. In this paper, we address this issue by introducing a flexible architecture to support researchers and practitioners in the task of designing real-world Recommender Systems that exploit social network data. We focus on detailing our proposed architecture modules and their interplay, potential algorithms for extracting and combining relevant social information, and candidate technologies for handling diverse and massive data volumes. Additionally, we provide an empirical analysis demonstrating the effectiveness of the proposed architecture on alleviating the cold-start problem over a concrete experimental case.
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Hu, Yuheng, and Yili Hong. "SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media." INFORMS Journal on Computing 34, no. 2 (March 2022): 790–806. http://dx.doi.org/10.1287/ijoc.2021.1112.
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Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.
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Bekemeier, Betty, and Seungeun Park. "Development of the PHAST model: generating standard public health services data and evidence for decision-making." Journal of the American Medical Informatics Association 25, no. 4 (November 2, 2017): 428–34. http://dx.doi.org/10.1093/jamia/ocx126.
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Abstract Objective Standardized data regarding the distribution, quality, reach, and variation in public health services provided at the community level and in wide use across states and communities do not exist. This leaves a major gap in our nation’s understanding of the value of prevention activities and, in particular, the contributions of our government public health agencies charged with assuring community health promotion and protection. Public health and community leaders, therefore, are eager for accessible and comparable data regarding preventive services that can inform policy decisions about where to invest resources. Methods We used literature review and a practice-based approach, employing an iterative process to identify factors that facilitate data provision among public health practitioners. Results This paper describes the model, systematically developed by our research team and with input from practice partners, that guides our process toward maximizing the uptake and integration of these standardized measures into state and local data collection systems. Discussion The model we developed, using a dissemination and implementation science framework, is intended to foster greater interest in and accountability for data collection around local health department services and to facilitate spatial exploration and statistical analysis of local health department service distribution, change, and performance. Conclusion Our model is the first of its kind to thoroughly develop a means to guide research and practice in realizing the National Academy of Medicine’s recommendation for developing systems to measure and track state and local public health system contributions to population health.
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Kline, Cathy, Wafa Asadian, William Godolphin, Scott Graham, Cheryl Hewitt, and Angela Towle. "From “Academic Projectitis” to Partnership: Community Perspectives for Authentic Community Engagement in Health Professional Education." Engaged Scholar Journal: Community-Engaged Research, Teaching, and Learning 4, no. 1 (May 28, 2018): 79–96. http://dx.doi.org/10.15402/esj.v4i1.310.
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Health professional education (HPE) has taken a problem-based approach to community service-learning with good intentions to sensitize future health care professionals to community needs and serve the underserved. However, a growing emphasis on social responsibility and accountability has educators rethinking community engagement. Many institutions now seek to improve community participation in educational programs. Likewise, many Canadians are enthusiastic about their health care system and patients, who are “experts by lived experience,” value opportunities to “give back” and improve health care by taking an active role in the education of health professionals. We describe a community-based participatory action research project to develop a mechanism for community engagement in HPE at the University of British Columbia (UBC). In-depth interviews and a community dialogue with leaders from 18 community-based organizations working with vulnerable populations revealed the shared common interest of the community and university in the education of health professionals. Patients and community organizations have a range of expertise that can help to prepare health practitioners to work in partnership with patients, communities, and other professionals. Recommendations are presented to enhance the inclusion of community expertise in HPE by changing the way the community and university engage with each other.
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Semisa, Domenico, Antonio Lora, Pierluigi Morosini, and Mirella Ruggeri. "The SIEP-DIRECT's Project on the discrepancy between routine practice and evidence in the treatment of schizophrenia. The design, the indicators, and the methodology of the study." Epidemiology and Psychiatric Sciences 17, no. 4 (December 2008): 278–90. http://dx.doi.org/10.1017/s1121189x00000099.
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SUMMARYAims– The aims of the SIEP-DIRECT'S Project (DIscrepancy betweenRoutine practice andEvidence in psychiatricCommunityTreatments onSchizophrenia) are: 1) To evaluate the appropriateness of the NICE guidelines for schizophrenia in Italian Departments of Mental Health; 2) to develop and to test a set of SIEP indicators, based on the NICE recommendations, useful to evaluate their real application in mental health services.Methods– Based on the NICE recommendations, 103 indicators have been developed, some of them qualitative and the rest quantitative. These indicators investigate five different areas: 14 indicators concern the common elements in all phases of mental health care; 11 the treatment of first episode; 24 the crisis treatment; 41 the recovery promotion; 13 the urgency, including management of violent behaviours and fast soothing. After a pilot study conducted in 2 community mental health services, the indicators were tested in 19 Italian Departments of Mental Health, to obtain a self-evaluation of the quality of care and to verify the application of NICE recommendations in the clinical routine of these Services. Data for the self-evaluation have been obtained from the DSM'ls psychiatric informative system and from the Direction of the local health authorities or of the DSM. Moreover, for some indicators, information has been gathered from the clinical records, and by means of questionnaires administered to a sample of patients and relatives. Finally, “multidisciplinary” (i.e. involving different types of professionals) or “specialized” (i.e. involving only psychiatrists) focus groups have assessed the degree of similarity between practice and recommendation for 33 indicators. Finally, the focus group methodology has been applied in all services with the aim to judge the appropriateness of each NICE recommendation in the context of the Italian Departments of Mental Health.Results and Conclusions– Most NICE recommendations have been considered useful and appropriated to measure quality of care in the context of the Italian services. The SIEP indicators have been easily used by the services participating in the DIRECT's Project. The self-evaluation process has provided several data of great relevance to improve the quality of care for schizophrenia and implement clinical guidelines in Italy.Declaration of Interest: None.
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Nurdin, Nurdin, Imam Suprayogi, Muhammad Shalahuddin, Ermiyati Ermiyati, Siswanto Siswanto, and Andre Novan. "Implementasi Model Soil dan Water Assessment Tool (SWAT) Dalam Pengelolaan Das Mikro Sei Tanduk." Dinamika Lingkungan Indonesia 10, no. 1 (January 31, 2023): 38. http://dx.doi.org/10.31258/dli.10.1.p.38-44.
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The Soil and Water Assessment Tool (SWAT) model which is integrated with the Geographic Information System (GIS) can provide an overview or prediction of the influence of land management on the hydrology of the Micro Sei Tanduk watershed in accordance with the interests of evaluating and monitoring watershed management activities. The purpose of this study was to analyze the management of the Sei Tanduk Micro watershed based on land use patterns, as a basis for determining the best recommendations for planning and evaluating the Indragiri Rokan Watershed Management Project Activities for Fiscal Year 2003 by compiling a Master Plan for the Micro Sei Tanduk Watershed Master Plan Rumbio Village, Kampar Regency. The research method is to process primary and secondary data using a SWAT model that is integrated with GIS in analyzing hydrological characteristics in land use scenarios in the management of the Micro Sei Tanduk watershed, as a basis for providing recommendations. The best recommendation from the evaluation and analysis of the Micro Sei Tanduk watershed management based on land use patterns is scenario 2 in the form of applying agroforestry on mixed dry land agriculture with the insertion of forestry trees based on community participation in the Micro Sei Tanduk watershed, Rubio Village, Kampar District, Kampar Regency.
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Lustig, Tracy. "Social Isolation and Loneliness: Strengthening Education and Training." Innovation in Aging 4, Supplement_1 (December 1, 2020): 714. http://dx.doi.org/10.1093/geroni/igaa057.2514.
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Abstract This paper reviews recommendations for a variety of opportunities to improve education and training of the health care workforce on the health impacts of SIL and clinical approaches for assessment as well as testing different approaches for such education and training. The fifth goal of the NASEM study is to “strengthen ties between the health care system and community-based networks and resources.” Similar to other social determinants of health, addressing SIL will require coordinated efforts among a variety of stakeholders. This paper reviews recommendations for improving coordination, including team-based care and promotion of tailored community-based services, as well as the creation of a centralized repository for new evidence and best practices. Finally, connections between this NASEM study and the 2019 report Integrating Social Care into the Delivery of Health Care are discussed. Part of a symposium sponsored by Loneliness and Social Isolation Interest Group.
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Paskali, Ljubomir, Lidija Ivanovic, and Dragan Ivanović. "Use analysis of the digital library of PhD dissertations defended at the University of Novi Sad." Electronic Library 39, no. 1 (February 8, 2021): 97–117. http://dx.doi.org/10.1108/el-11-2019-0268.
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Purpose The purpose of this paper is to determine the digital library usage patterns as a means of improving the system, as well as the user experience, to give appropriate recognition to the most popular dissertations’ authors and to measure the interest of non-academic users for dissertations defended at the University of Novi Sad (UNS). Design/methodology/approach A logging module of the digital library of theses and dissertations of University of Novi Sad (PHD UNS) application has been implemented. The module recorded the messages relating to the search queries and downloads over a three-year period from 2017–2019. These logs are analysed using the Elasticsearch, Logstash and Kibana (ELK) technology stack and the results are shown using graphs and tables. Findings The analysis determined the perfect time for weekly maintenance of the system, defined a recommendation for improving the system and revealed the most popular dissertations. A significant number of downloads and queries originated from citizens, i.e. users outside the academic community. Practical implications The conducted analysis defined recommendations for the system improvement which can be used by PHD UNS research and development (R&D) team and revealed the most popular dissertations which are used for the promotion of its authors through faculties’ websites. Originality/value To the best of the authors’ knowledge, this is the first study of ELK based log analysis of a Serbian language documents’ repository. Besides, the value of results for the PHD UNS R&D team and UNS rector team, the study proves that PhD digital library presents an important Open Science communication channel for presenting scientific results to the citizens.
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Zoppis, Italo, Andrea Trentini, Sara Manzoni, Daniela Micucci, Giancarlo Mauri, Giada Pietrabissa, and Gianluca Castelnuovo. "Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations." Applied Sciences 10, no. 9 (April 26, 2020): 3024. http://dx.doi.org/10.3390/app10093024.
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Conscious and functional use of online social spaces can support the elderly with mind cognitive impairment (MCI) in their daily routine, not only for systematic monitoring, but to achieve effective targeted engagement. In this sense, although social involvement can be obtained when elder’s experiences, interests, and goals are shared and accepted by the community, an important subsistence for aging depends on the compelling information, users’ co-operation, and resource reliability. Unfortunately, applications aimed at optimizing the information content and the reliability of online users are still missing. Within the SystEm of Nudge theory-based ICT applications for OldeR citizens (SENIOR) project, an advanced social platform will be created in which the elderly with MCI will be involved in “optimized” social communities, where suggestions for general well-being will be recognized as useful by users and shared by care providers. We report the results of our study addressing this issue from a theoretical perspective: we propose a computational problem and a heuristic solution where “expert users” can engage and support the elderly by suggesting available services and facilities for their conditions. The numerical experiments on synthetic data are of interest when considering large communities, which is the most natural situation for online social spaces.
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Perez-Rodrigo, Carmen, Marta Gianzo Citores, Gotzone Hervás Bárbara, and Javier Aranceta-Bartrina. "Environmental Impact of Increased Adherence to SENC Food Based Dietary Guidelines in the Average Dietary Patterns in Spain." Current Developments in Nutrition 5, Supplement_2 (June 2021): 563. http://dx.doi.org/10.1093/cdn/nzab043_015.
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Abstract Objectives To analyze estimated changes in greenhouse gas (GHG) emission, cropland use and freshwater use of greater adherence to recommendations in Food-Based Dietary Guidelines (FBDG) issued by the Spanish Society of Community Nutrition (SENC) in average dietary patterns in Spain. Methods The data analyzed was drawn from the ENPE study (n = 6800), a cross-sectional study conducted on a random population sample of Spanish population (2015). Dietary intake data was collected by means of a validated 210-item quantitative food frequency questionnaire. A score of adherence to recommendations in SENC FBDG (DG score) was computed (DG score range 0 to 16). Based on food group intake and adherence to each quantitative recommendation, we estimated food group consumption for a 20% increase in adherence to FBDGs. We calculated the potential environmental impact for those estimates, considering data available on crop specific environmental footprints for greenhouse gas emissions, cropland use and freshwater use. Results SENC FBDGs were formulated in tune with current food, cultural, gastronomic, and economic context. Changes in usual diets that people could comfortably afford in the short and medium term were suggested. Mean adherence DG score in this sample was 7.43 ± 1.94 (median = 7). Dietary changes required for increased adherence DG score included decreased consumption of red and processed meats as well as sugary foods and beverages. In addition, higher consumption of whole grains, fish, eggs, legumes, fruit, vegetables and nuts. Such dietary changes were estimated to contribute to 24% reduction in GHG emissions, but 16% increase in cropland use and 25% in freshwater use. Conclusions FBDG are useful tools for preventive actions and health promotion, helpful for guidance in health, education, and community settings. Despite limited evidence, increased adherence to SENC FBDGs can contribute to reduce GHG emissions. Analysis of impact on a variety of indicators of sustainability, comprehensive food system view and consumer involvement are needed to gain adherence to FBDGs and additional impact on sustainability. Funding Sources Non-for-profit Eroski Foundation funded the ENPE study field work in collaboration with SPRIM. Funders had no role in data analysis, selection or interpretation of results. The authors declare no competing interests.
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Meidl, Michael, Steven Lytinen, and Kevin Raison. "Using Game Reviews to Recommend Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 4 (June 29, 2021): 24–29. http://dx.doi.org/10.1609/aiide.v10i4.12752.
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We present a recommender system intended to be used by a community of gamers. The system uses free-form text reviews of games written by the members of the community, along with information about the games that a particular user likes, in order to recommend new games that are likely to be of interest to that user. The system uses the frequency of co-occurrence of word pairs that appear in the reviews of a game as features that represent the game. The pairs consist of adjectives and context words; i.e., words that appear close to an adjective in a review. Because of the extremely large number of possible combinations of adjectives and context words, we use information-theoretic co-clustering of the adjective-context word pairs to reduce the dimensionality. Games are represented using the standard information retrieval vector space model, in which vector features are based on the frequency of occurrence of cocluster pairs.We present the results of three experiments with our system. In the first experiment, we use a variety of strategies to relate frequencies of co-cluster pairs to vector features, to see which produces the most accurate recommendations. In the second, we explore the effects of co-cluster dimensionality on the quality of our system’s recommendations. In the third experiment, we compare our approach to a baseline approach using a bag-of-words technique and conclude that our approach produces higher quality recommendations.
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Mariyam, Sabah, Logan Cochrane, Shifa Zuhara, and Gordon McKay. "Waste Management in Qatar: A Systematic Literature Review and Recommendations for System Strengthening." Sustainability 14, no. 15 (July 22, 2022): 8991. http://dx.doi.org/10.3390/su14158991.
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Billions of tons of waste are generated annually, with the amount of waste rapidly increasing and its management expected to worsen. Qatar is a small and wealthy country in the Arabian Peninsula that is undergoing enormous economic and urban development. This study presents the results of a systematic literature review on waste management in Qatar and offers recommendations for system strengthening based on an analysis of the available evidence. The PRISMA guidelines were followed to review literature from the ProQuest and SCOPUS platforms, from which 82 unique publications were analyzed according to ten themes. The results draw upon diverse disciplinary and research focus areas related to waste management, ranging from the generation of value-added products from wastes to the role of religion in waste management awareness. The main recommendations emerging from the available evidence include the need for a holistic approach to address increasing waste generation, which must include diverse stakeholders such as government entities, researchers, and broad community representation for decision making as well as raising awareness for behavior change. Additionally, the main waste types, including construction, food, plastic, and electronic wastes, require specific attention since the cause of generation and type of management varies accordingly. The rise of interest in improving waste management, in particular to work toward meeting the targets of the Qatar National Vision and the National Development Strategy should bring about positive outcomes for strengthening waste management systems.
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Galambos, Colleen, and James Lubben. "Social Isolation and Loneliness in Older Adults: A National Academies of Sciences, Engineering, and Medicine Report." Innovation in Aging 4, Supplement_1 (December 1, 2020): 713. http://dx.doi.org/10.1093/geroni/igaa057.2511.
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Abstract Social isolation and loneliness (SIL) are serious yet underappreciated public health risks for many older adults (AARP, 2018a). Strong evidence suggests that, for older adults, social isolation and loneliness are associated with an increased likelihood of early death, dementia, heart disease, and more (AARP, 2018b, Holt-Lunstad and Smith, 2016). While all ages may experience SIL, older adults are at increased risk because they are more likely to face predisposing factors such as living alone, the loss of family or friends, chronic illness, and sensory impairments. Health care providers may be in the best position to identify older individuals who are at highest risk for SIL – individuals for whom the health care system may be the only point of contact with their broader community. The National Academies of Sciences, Engineering, and Medicine (NASEM) developed a consensus study report on this issue. This symposium presents the study recommendations. Dr. Holt-Lunstad examines the recommendations to develop a more robust evidence base for effective assessment, prevention, and intervention strategies for social isolation and loneliness. Dr. Galambos examines the recommendations to translate current research into health care practices and to improve awareness of the health and medical impacts of SIL. Dr. Lustig examines the recommendations to strengthen ongoing education and training and to strengthen ties between the health care system and community-based resources. Dr. Demiris examines the role of technology across all of these recommendations. Loneliness and Social Isolation Interest Group Sponsored Symposium
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S. Bayne, Clarence, and Raafat G. Saade. "The Use of Complex Adaptive Theory and Information Technologies to Inform Development Strategies in English Speaking Black Community, Montreal." International Journal of Community Development and Management Studies 2 (2018): 061–86. http://dx.doi.org/10.31355/23.
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NOTE: THIS ARTICLE WAS PUBLISHED WITH THE INFORMING SCIENCE INSTITUTE. Aim/Purpose................................................................................................................................................................................ The purpose of this paper is to conduct a multi-case/agent analysis using complexity theory to develop propositions that guide and inform our research for solutions to the problems of integration and full participation of the English-speaking Black community in the societies of Montreal and Quebec. Background................................................................................................................................................................................ This study was motivated by our interest in community organizational leader-ship, and concerns expressed by Black social entrepreneurs and organizations in the English-speaking Black communities of Montreal. The results of an unpublished survey conducted by the Institute for Community Entrepreneurship and Development (ICED) revealed a strong perception among Black leaders that in spite of their efforts to advance their communities there was too little progress. They attributed this to systemic exclusion and competitive strategies of mainstream non-Black agencies and leaders. Our further investigation of these claims suggested that beside discrimination based on color and race, factors more complex than skin color, being a person of African descent or White hate, were at work. Preliminary patterns in our observations suggest that the problems of exclusion and discrimination needed to be addressed in a broader psycho-social sense and in the context of Canada as a complex political, economic, and social adaptive system emerging continuously from generation to generation Methodology................................................................................................................................................................................ We used historical analysis and dynamic systems constructs to understand the causality structures of human social systems and to design strategies that have the highest possibilities for improving and optimizing the objective and subjective well-being of members of targeted minority sub-groups in the system. The general research approach is deductive and exploratory. It conforms mostly to critical realist thinking as opposed to traditional scientific methodologies. Contribution................................................................................................................................................................................ It is our opinion that communication network centers can be designed as part of a strategic planning process to increase the capacity of minority communities for creating, in a timely manner, the ingenuity required for solving problems of social, political and economic exclusion; for promoting sustainable development and improving objective and subjective well-being. The use of the MAS (multiple-agents system) analytical framework allows us to address and assess problems of decision making under varying degrees of uncertainty and in the social and historical context of the study. Findings..................................................................................................................................................................................... Our review of the development and progress of the Black community of Montreal shows that “under the radar” community based organizations and Black Social entrepreneurs have developed governance mechanisms and generated strategies and approaches to decision making that are consistent with the optimal patterns observed in simulations of multi-agent systems (MAS) . In particular, social entrepreneurs seem to support the formal creation of community based communication networks and information sharing as essential for community development. Several of these organizations consider these useful tools for facilitating the sharing of innovative ideas and best practices. Recommendations for Practitioners.......................................................................................................................................... The usefulness of the network community systems need to be monitored. Its usefulness will depend on how its outputs are perceived to have contributed to improving the level of fitness (the vitality and well-being/utility) of the community and its members. It will require a holistic approach to community development supported by network centers that provide communication and information services at levels that improve and sustain the capacity of the organizations and the community to adapt and evolve from generation to generation. The mechanisms in place must increase and sustain the capacity of the systems to achieve and maintain the desired level of outcomes consistent with attaining the highest fitness levels for the English speaking Black Communities. This must be tested with the help of information provided by a built in feedback subsystem of the network. Recommendation for Researchers.............................................................................................................................................. A central database has to be built into the system where social and economic data and measures of subsystem specific attributes and characteristics are gathered and stored for use by the network organizations and social entrepreneurs. There is no comprehensive measure of a fitness index for the Black community in Montreal. Theoretically speaking, there are too many possibilities to find a precise solution. However, an approximation of fitness can be obtained by constructing a human development index (HDI) in combination with measures of inequality such as comparative data on income, employment and unemployment, poverty, and etc. Impact on Society......................................................................................................................................................................... The paper raises some questions about the success of the experiment of multiculturalism in terms of greater recognition of the contributions of Canada’s diverse and multiple sub-cultures. It proposes ways to address complaints of failed expectations expressed by Black and immigrant minority groups. The paper offers policy makers and social entrepreneurs a dynamic analytical framework to explore the use of information and communication network theory, and information from simulations of multi-agent adaptive systems theory to develop more informed strategies and actions. Future Research............................................................................................................................................................................ More research needs to be done to improve the quality and expand the demographic and other data relating to the black communities in Montreal and Quebec. In addition, more research needs to be done on the development of an archival documentation system for the management and distribution of information between the different communities that make up the Black cultural community of Quebec and Canada.
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Anugrah, Rinasa Agistya. "Troubleshooting Analysis of CVT (Continuously Variable Transmission) System on Honda Beat Motorcycles." MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering 4, no. 1 (October 27, 2021): 1–8. http://dx.doi.org/10.46574/motivection.v4i1.98.
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There are about 87% of motorcycles used by motorists on the streets are automatic type motorcycles which are dominated by Honda Beat. Seeing the interest of the world community about their interest in automatic motorcycles, it is necessary to conduct research to analyze the CVT (Continuously Variable Transmission) system on this motorcycle. The method used in this research is the troubleshooting analysis method. Troubleshooting is a method for observing symptoms, diagnosing damage, conducting inspections and measurements, identifying damage based on the results of inspections and measurements, and determining corrective steps if repairs must be made. The results of this study are recommendations for troubleshooting steps on the Honda Beat CVT system. With this troubleshooting step, technicians have a reference to carry out maintenance and repair of Honda Beat automatic motorcycles, especially on the CVT system.
Terdapat sekitar 87% sepeda motor yang digunakan pengendara dijalanan adalah sepeda motor tipe matic yang didominasi oleh Honda Beat. Melihat animo masyarakat dunia tentang minat mereka terhadap sepeda motor matic maka perlu dilakukan penelitian untuk menganalisis sistem CVT (Continuously Variable Transmission) pada sepeda motor ini. Metode yang digunakan dalam penelitian ini adalah metode analisis troubleshooting. Troubleshooting adalah metode untuk mengamati gejala, mendiagnosis adanya kerusakan, melakukan pemeriksaan dan pengukuran, mengidentifikasi kerusakan berdasarkan hasil pemeriksaan dan pengukuran, serta menentukan langkah perbaikan jika harus dilakukan perbaikan. Hasil dari penelitian ini adalah rekomendasi langkah troubleshooting pada sistem CVT Honda Beat. Dengan Langkah troubleshooting ini para teknisi memiliki acuan untuk melaksanakan perawatan dan perbaikan sepeda motor matic Honda Beat khususnya pada sistem CVT.
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Malami SarkinTudu, Shehu, Alawiyah Abd Wahab, and Huda H Ibrahim. "Predicting Key Predictors of Project Desertion in Blockchain: Experts’ Verification Using One-Sample T-Test." Interdisciplinary Journal of Information, Knowledge, and Management 17 (2022): 497–521. http://dx.doi.org/10.28945/5022.
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Aim/Purpose: The aim of this study was to identify the critical predictors affecting project desertion in Blockchain projects. Background: Blockchain is one of the innovations that disrupt a broad range of industries and has attracted the interest of software developers. However, despite being an open-source software (OSS) project, the maintenance of the project ultimately relies on small core developers, and it is still uncertain whether the technology will continue to attract a sufficient number of developers. Methodology: The study utilized a systematic literature review (SLR) and an expert review method. The SLR identified 21 primary studies related to project desertion published in Scopus databases from the year 2010 to 2020. Then, Blockchain experts were asked to rank the importance of the identified predictors of project desertion in Blockchain. Contribution: A theoretical framework was constructed based on Social Cognitive Theory (SCT) constructs; personal, behavior, and environmental predictors and related theories. Findings: The findings indicate that the 12 predictors affecting Blockchain project desertion identified through SLR were important and significant. Recommendations for Practitioners: The framework proposed in this paper can be used by the Blockchain development community as a basis to identify developers who might have the tendency to abandon a Blockchain project. Recommendation for Researchers: The results show that some predictors, such as code testing tasks, contributed code decoupling, system integration and expert heterogeneity that are not covered in the existing developer turnover models can be integrated into future research efforts. Impact on Society: This study highlights how an individual’s design choices could determine the success or failure of IS projects. It could direct Blockchain crypto-currency investors and cyber-security managers to pay attention to the developer’s behavior while ensuring secure investments, especially for crypto-currencies projects. Future Research: Future research may employ additional methods, such as a meta-analysis, to provide a comprehensive picture of the main predictors that can predict project desertion in Blockchain.
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Wernigg, R., M. D. Toth, and A. Sumegi. "Demonstrating the methodology of a pilot programme for establishing a system for mental health promotion in Hungary." European Psychiatry 41, S1 (April 2017): S741—S742. http://dx.doi.org/10.1016/j.eurpsy.2017.01.1368.
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IntroductionA 12 month long mental health promotion pilot programme in Hungary, funded by the Norway Grants, will finish on 30th April 2017.AimsTo launch six community mental health promotion centers (MHPCs) located in various economic environments, supervised by one Methodological Center plus expert teams and carrying out studies on the effectiveness of the launch of the system. The final recommendations will constitute a basis for setting up a national network for mental health promotion.ObjectivesTo develop a sustainable model for establishing a national network of MHPCs; to map and record the mental health problems and the stakeholders of the area; to find and assess the local best practices; and to raise public awareness in the following mental health problems: depression, suicide, stress and dementia.Methods(1) Models for inducing changes in the knowledge and attitudes upon mental health in the communities were reviewed in a systematic literature search. (2) Based on the results, a model of mental health promotion centers was outlined for problem identification, stakeholder mapping, assessing local best practices, organizing and supporting local networking activities for synergistic effects. (3) In the new MHPCs, on-site TTT education programme and organisational development have been in place. (4) The activities of the MHPCs have been monitored for knowledge transfer and attitude changes in the local networks. (5) Based on the results, recommendations are being made for the national extension of the model.Results and conclusionsThe first data ready for analysis will be available by April 2017.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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Lukin, A., and O. Pugacheva. "Korea in the Early 21st Century and Russia’s Interests." International Trends / Mezhdunarodnye protsessy 18, no. 4 (2020): 143–57. http://dx.doi.org/10.17994/it.2020.18.4.63.6.
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The article is a response to a new, pioneering book Modern Korea. Metamorphoses of Turbulent Years (2008–2020) written by a group of leading Russian experts on Korea from MGIMO University: Anatoly Torkunov, Georgy Toloraya, and Ilya Dyachkov. The book is a valuable addition to the existing literature and a product of a unique approach to modern Korean studies conducted at MGIMO. The article examines and develops the comprehensive analysis provided by MGIMO professors, point out pressing issues on the Korean Peninsula and assess Russia’s potential role in solving them. The fact that the goal of denuclearizing the Korean Peninsula based on Pyonyang’s rejection of nuclear weapons not only has not been achieved, but became even more distant than before, leads us to questioning the logic behind the authors' recommendation for Russia to withdraw its support from the international system of sanctions against Pyongyang and to move closer to North Korean. We argue that easing the sanctions may mean recognizing that Russia does not believe in this goal and wishes to encourage North Korea's refusal to comply with the demands of the international community. Moreover, such an approach could be perceived as evidence of Russia’s support of some new academic theories which claim that the very system of nonproliferation has become outdated and can even be abolished altogether. At the same time, this position would reduce the role of Russia in the Korean settlement, which would inevitably have a negative impact on DPRK's renunciation of nuclear weapons in the foreseeable future. The article argues that such views, especially that the non-proliferation strategy is outdated, come into a direct contradiction with Russia's interests. The recognition of DPRK's nuclear status may spark a chain reaction in the quest for nuclear weapons in the region which in turn would create a serious security threat for Russia's eastern regions. In addition, an increase in the number of nuclear states would devalue Russia’s status as a nuclear power, thus, the policy of nonproliferation of nuclear weapons should remain a priority of Russia’s foreign policy. Overall, we conclude that Moscow's policy towards the Korean peninsula should be based on careful evaluation of the current international situation as well as Russia’s interests, and not on outdated and often counterproductive Soviet tradition.
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V. Tselyutina, Tatyana, Tatyana A. Vlasova, Olga A. Timokhina, and Yana V. Maslova. "CONVERGENCE OF INSTITUTES OF FUNCTIONAL REPRESENTATION OF GROUP INTERESTS UNDER TRANSFORMATION OF SOCIAL PROCESSES, QUALITY OF LIFE, AND HUMAN CAPITAL." Humanities & Social Sciences Reviews 7, no. 6 (November 17, 2019): 111–17. http://dx.doi.org/10.18510/hssr.2019.7624.
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Purpose: This article substantiates the influence of mechanisms, methods, and technologies of a functional representation of group interests on the strategic management of socio-economic processes in the context of geopolitical turbulence.
Methodology: The work defines the socio-stratification basis for the representation of group interests, their role as subjects of representation in the modern system of management of social processes, the features of the convergence of functional representation of various interest groups, first of all, the multiform structures of the consulting and expert system, and ways to improve their performance subject to current reforming trends towards improvement of the quality of life and update of the importance of human capital..
Result: The authors determined the dependence of the results of managing socio-economic processes, the strategy of sustainable economic development of Russia on the degree of conscious complicity, social activity of the population and the presence of relevant institutions of functional representation of group interests, transparent (open, mutually beneficial, corruption-free) management mechanisms of a particular region. Government authorities, functional representation institutions can use the scientifically based recommendations proposed by the authors in the development of socio-economic projects and programs aimed at effective, first of all, regional development, and at the establishment of open channels of interaction in a “society - business - state” symbiosis. The article will also be of assistance to the scientific community as a theoretical justification of practical actions aimed at developing the institutions of a functional representation of group interests
Applications: This research can be used for the universities, teachers, and students.
Novelty/Originality: In this research, the model of convergence of institutes of a functional representation of group interests under transformation of social processes, quality of life, and human capital is presented in a comprehensive and complete manner.
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Corder-Mabe, Joan, Shirley Johnson, Wally R. Smith, Daniel Sop Mouaffo, and Thokozeni Lipato. "Development of a Framework to Describe Roles and Responsibilities for Sickle Cell Community Health Workers." Blood 132, Supplement 1 (November 29, 2018): 5831. http://dx.doi.org/10.1182/blood-2018-99-114944.
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Abstract Background: Evidence is mounting for the effectiveness of community health workers (CHWs) at improving processes and outcomes of care in sickle cell disease (SCD) and other chronic diseases. But recognition is stymied by over 60 titles currently used for CHWs, by variable CHW roles and responsibilities in SCD and other disease entities, and by variations in populations and communities served by CHWs. Herein, we use an evidence review and consensus to advance a common framework and uniform definition of the functions of CHWs, that differentiates CHW roles and responsibilities. We offer the justification for this differentiation, and for certification, credentialing, education, licensure, and payment for CHW service in the US. Methods: To standardize the CHW intervention in the NHLBI-funded Start Healing in Patients with Hydroxyurea (SHIP-HU,R18HL112737) randomized controlled trial, we reviewed existing publications related to CHW efficacy. In order to synthesize and codify this efficacy evidence according to CHW occupational activities, we performed a rapid, narrative and tabular review of the clinical trials, meta-analyses and policy consensus reports summarizing over 200 CHW interventions to improve patient health status or care delivery. We built frequency tables to total how many mentions occurred of each of the specific roles, responsibilities, competencies, and behaviors utilized in these interventions, using a predetermined list built from a review of all the included interventions. Results: (Evidence table too large to show) Findings of our review and consensus: 1) the more frequently mentioned intervention behaviors might be more important or generally required of all CHWs, whereas the rarer behaviors might either be more specialized or might be less often required of all CHWs; 2) there is strong evidence that CHWs are effective in providing culturally competent health education to individuals and groups, as well as health system navigation and care coordination; 3) there is not strong evidence for the efficacy of CHWs in the provision of direct services; 4) Coaching and social support were less commonly mentioned than the above services; 5)Neither outreach, case finding, nor advocacy were often mentioned in interventions, and; 6) Participation and evaluation in research was mentioned least in interventions. Our consensus recommendation based on these data is for a common framework or taxonomy consisting of four levels of CHW function: Peer Community Health Worker (PCHW), General Community Health Worker (GCHW), Clinical Community Health Worker (CCHW), and Health Navigator (HN). Conclusions: Our proposed evidence-based CHW taxonomy standardizes definitions, provides categories into which to place workers, and delineates levels of service that, if tested, validated, and adopted, could allow national standards for CHW scope of practice, licensure, certification, and registration to practice. Disclosures No relevant conflicts of interest to declare.
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Obidoa, Mabel. "Infusing Talent Development Strategies into the Regular School System in Nigeria: Enrichment Clusters as a Starting Point." Gifted Education International 17, no. 3 (September 2003): 297–312. http://dx.doi.org/10.1177/026142940301700312.
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Developing talents has been a great concern in education. New conceptions of intelligence are revolutionizing educational practices. Because giftedness can occur in certain people at certain times and under certain conditions, (Renzulli & Reis, 1985), it is necessary to extend the pedagogy for the gifted to regular classrooms. The enrichment cluster was chosen for this study for multiple reasons. This study used qualitative case study design to investigate the use of enrichment clusters as a starting point to infuse talent development strategies into the school system in Nigeria. The sample was made up of 27 boys and 33 girls between the ages of 13 and 19 years in Senior Secondary School (grades 10 to 12) drawn from different socio-economic backgrounds but from educated parents. Their common denominator was their interest in the cluster topic — an essential ingredient in any pupil programme. The study found that a lone enrichment specialist in a regular school without programmes for the gifted and talented can expose students to high-order thinking skills and high-end learning through the use of Renzulli's Type I, II & III enrichments in an enrichment cluster. Parents and members of the community were found to be very helpful in conducting the enrichment cluster. The possibility of these people being advocates for programmes for talent development and mentors is discussed. Based on the findings, recommendations were made for further research.
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Kulyasova, E. V., and P. V. Trifonov. "DEVELOPMENT OF FORMS OF INTERACTION BETWEEN UNIVERSITIES AND THE BUSINESS COMMUNITY IN THE DIGITAL ECONOMY." Strategic decisions and risk management 11, no. 2 (September 23, 2020): 216–23. http://dx.doi.org/10.17747/2618-947x-2020-2-216-223.
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The article is devoted to the construction of a model of interaction of economic entities using the method of project work of participants in the system “enterprise/employer-University – state” on the technological platform of the meta-University in the conditions of digitalization of the economy, which allows to increase the synergy effect of the interaction of the main institutional participants.In the context of the impact of digitalization processes on the transformation of behavioral models within the existing economic relations in the VUCA world, new requirements and ways of interaction of subjects of the triple helix model are considered. The article analyzes the main existing and prospective forms of cooperation between universities and business structures. The detailed description of barriers and difficulties on the way of digitalization of enterprises and scientific organizations is given.For employees of scientific and commercial enterprises in the conditions of digitalization and General increased uncertainty, recommendations are formulated for the choice of a behavior model that adapts their professional identity based on the principles of a proactive position and knowledge and skills at the intersection of different technological directions.Under the conditions of variability and uncertainty of the environment, a scientific problem has been identified, there is a high interest in collaboration between universities and businesses, but there is no mechanism that allows this interaction to be carried out with a high degree of efficiency.A set of mechanisms that help reduce the level of uncertainty is proposed, as well as a project method of interaction within the framework of the digital meta-University technology platform model is described.The article provides recommendations for the full implementation of human capital in the new technological conditions of economic and social development, in the so-called VUCA-world, which is characterized by the lack of a developed digital infrastructure, as well as a high level of transaction costs.A new approach is proposed, based on which participants will interact on the basis of shared access to information and digital resources and the ability to combine the development of innovative projects and training of personnel necessary to unite the University, enterprises and scientific organizations to reduce transaction, fixed and variable costs of participants in the process.
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Paksi, Arie Kusuma, Nanik Prasetyoningsih, and Dianita Sugiyo. "Tobacco Control Politics in Indonesia: Regional and Global Perspective." Nation State Journal of International Studies 2, no. 2 (December 31, 2019): 187–209. http://dx.doi.org/10.24076/nsjis.2019v2i2.166.
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The study examines tobacco control policies of Indonesia, national and international players, the role of political elite and social demography of smoking. The analysis explains the relation of national and international players associated with the tobacco industry, interest of local community, structural and political powers that limit the legislation on tobacco control. The current situation of smoking and tobacco use in the social system of Indonesia is alarming. Indonesia is the working ground of different multinational brands that have industries and production units of cigarettes. The economic perspectives of the tobacco industry involvement of government and political powers in this industry make it more complex. The study is based on qualitative sources that are evaluated for the tobacco industry and its implications on Indonesian society. The results provide a comprehensive overview of the tobacco industry and the role of different actors that can be regularized by imposing strict legislation. The study also provides recommendations to manage the tobacco industry that will guide the corrective path of reducing tobacco culture in the society.