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Journal articles on the topic 'Group Recommendation System'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Dewi, Ratih Kartika. "Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System." Jurnal Ilmu Komputer dan Informasi 12, no. 2 (July 8, 2019): 85. http://dx.doi.org/10.21609/jiki.v12i2.729.

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This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation
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Dewi, Ratih Kartika, Eriq Muhammad Adams Jonemaro, Agi Putra Kharisma, Najla Alia Farah, and Mury Fajar Dewantoro. "TOPSIS for mobile based group and personal decision support system." Register: Jurnal Ilmiah Teknologi Sistem Informasi 7, no. 1 (February 15, 2021): 43. http://dx.doi.org/10.26594/register.v7i1.2140.

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Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.
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Kim, Jae Kyeong, Hyea Kyeong Kim, Hee Young Oh, and Young U. Ryu. "A group recommendation system for online communities." International Journal of Information Management 30, no. 3 (June 2010): 212–19. http://dx.doi.org/10.1016/j.ijinfomgt.2009.09.006.

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Thenmozhi, M., and T. P. "Group Coupon Recommendation System for Mobile Users." International Journal of Computer Applications 143, no. 10 (June 17, 2016): 31–36. http://dx.doi.org/10.5120/ijca2016910379.

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Liu, Xuehong, and Xuefeng Ding. "User Privacy Protection Algorithm Of Perceptual Recommendation System Based On Group Recommendation." International Journal of Autonomous and Adaptive Communications Systems 13, no. 2 (2020): 1. http://dx.doi.org/10.1504/ijaacs.2020.10031495.

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Ding, Xuefeng, and Xuehong Liu. "User privacy protection algorithm of perceptual recommendation system based on group recommendation." International Journal of Autonomous and Adaptive Communications Systems 13, no. 2 (2020): 135. http://dx.doi.org/10.1504/ijaacs.2020.109809.

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Dewi, Ratih Kartika, Mahardeka Tri Ananta, Lutfi Fanani, Komang Candra Brata, and Nurizal Dwi Priandani. "The Development of Mobile Culinary Recommendation System Based on Group Decision Support System." International Journal of Interactive Mobile Technologies (iJIM) 12, no. 3 (July 20, 2018): 209. http://dx.doi.org/10.3991/ijim.v12i3.7799.

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Mobile based culinary recommendation system has received significant attention in recent mobile application research . Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has regained popularity in supporting multi-criteria decision making due to this method allowing inclusion of many factors and criteria into the decision making process. Previous works on mobile based scenario culinary recommendation system reveal that TOPSIS stand out from other recommendation approaches like AHP and Fuzzy by providing a lightweight computation algorithm that have promising performance in time complexity. However, computing a culinary recommendation using TOPSIS has own limitations especially in the menu judgment processes due to the alternatives priority only include personal preferences for recommendation. In such a culinary recommendation system scenario, users more likely search culinary menus in group instead of alone. This research aims to develop a culinary recommendation system based on group decision support system (GDSS) using TOPSIS that possible to calculate a recommendation by using group preferences instead of personal preferences. The experimental results show that the overall functional of proposed GDSS gives better recommendation result. GDSS using TOPSIS have 100% rank consistency for 6 group of users with 5 combination of menus. The accuracy testing shows that 83,33 % recommendation of GDSS TOPSIS are match with real user preferences. Furthermore, it can be run well in various type of Android smartphone.
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Ravi, Logesh, and Subramaniyaswamy Vairavasundaram. "A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users." Computational Intelligence and Neuroscience 2016 (2016): 1–28. http://dx.doi.org/10.1155/2016/1291358.

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Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.
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Meena, Ritu, and Sonajharia Minz. "Group Recommender Systems – An Evolutionary Approach Based on Multi-expert System for Consensus." Journal of Intelligent Systems 29, no. 1 (November 20, 2018): 1092–108. http://dx.doi.org/10.1515/jisys-2018-0081.

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Abstract Recommender systems have focused on algorithms for a recommendation for individuals. However, in many domains, it may be recommending an item, for example, movies, restaurants etc. for a group of persons for which some remarkable group recommender systems (GRSs) has been developed. GRSs satisfy a group of people optimally by considering the equal weighting of the individual preferences. We have proposed a multi-expert scheme (MES) for group recommendation using genetic algorithm (GA) MES-GRS-GA that depends on consensus techniques to further improve group recommendations. In order to deal with this problem of GRS, we also propose a consensus scheme for GRSs where consensus from multiple experts are brought together to make a single recommended list of items in which each expert represents an individual inside the group. The proposed GA based consensus scheme is modeled as many consensus schemes within two phases. In the consensus phase, we have applied GA to obtain the maximum utility offer for each expert and generated the most appropriate rating for each item in the group. In the recommendation generation phase, again GA has been employed to produce the resulting group profile, i.e. the list of ratings with the minimum sum of distances from the group members. Finally, the results of computational experiments that bear close resemblance to real-world scenarios are presented and compared to baseline GRS techniques that illustrate the superiority of the proposed model.
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Chen, Yen-Liang, Li-Chen Cheng, and Ching-Nan Chuang. "A group recommendation system with consideration of interactions among group members." Expert Systems with Applications 34, no. 3 (April 2008): 2082–90. http://dx.doi.org/10.1016/j.eswa.2007.02.008.

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VILLAVICENCIO, Christian Paulo, Silvia SCHIAFFINO, J. Andrés DÍAZ-PACE, and Ariel MONTESERIN. "A Group Recommendation System for Movies based on MAS." ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL 5, no. 3 (November 15, 2016): 1. http://dx.doi.org/10.14201/adcaij201653112.

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RODRÍGUEZ MARÍN, Paula Andrea, Mauricio GIRALDO, Valentina TABARES, Néstor DUQUE, and Demetrio OVALLE. "Educational Resources Recommendation System for a heterogeneous Student Group." ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL 5, no. 3 (November 15, 2016): 21. http://dx.doi.org/10.14201/adcaij2016532130.

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13

Marques, Gabriel, Ana Respício, and Ana Paula Afonso. "A Mobile Recommendation System Supporting Group Collaborative Decision Making." Procedia Computer Science 96 (2016): 560–67. http://dx.doi.org/10.1016/j.procs.2016.08.235.

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14

Hiray, Prof S. R. "Book Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1981–83. http://dx.doi.org/10.22214/ijraset.2021.39658.

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Abstract: Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.
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Teoman, Huseyin Alper, and Pinar Karagoz. "Trust-aware location recommendation for user groups." ACM SIGAPP Applied Computing Review 22, no. 3 (September 2022): 39–55. http://dx.doi.org/10.1145/3570733.3570736.

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Recommender systems have become indispensable part of online environments. Product recommendations in ecommerce applications, location recommendations in location related applications, and user and post recommendations in online social networks are just a few examples in the world of online recommendations. The recommendations generated in such environments are mostly for individuals. However, increasing use of social networks and online communities lead to need for generating recommendations for a group of users for joint activities, such as eating out as a group or seeing a movie with friends. In the literature, there are studies proposing recommendation solutions for groups, but the number of such studies is very limited. In this work, we address the problem of location recommendation to a group of users, and the proposed solutions are based on Random Walk with Restart (RWR) algorithm on the social network graph. Another novel aspect of the proposed work is the use of trust factor of users in location-based social networks (LBSNs). In generating group recommendations, we follow two alternative paths. The first one aggregates the location recommendations that are generated with the Random Walk algorithm for each member in the group, taking the preferences and objectivity scores of the individuals into account. The second one is based on creating a group profile by blending preferences and venue category types, and using this group profile to run the Random Walk algorithm once. In both approaches trust factor of users is incorporated into the solutions within the social network graph. The experiments conducted on the data collected from the location based social network platform Foursquare have shown that the trust factor of users improves the performance of group recommendation system and the proposed algorithm provides recommendations to groups with high accuracy compared to the baselines.
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Contreras, David, Maria Salamó, and Ludovico Boratto. "Integrating Collaboration and Leadership in Conversational Group Recommender Systems." ACM Transactions on Information Systems 39, no. 4 (October 31, 2021): 1–32. http://dx.doi.org/10.1145/3462759.

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Recent observational studies highlight the importance of considering the interactions between users in the group recommendation process, but to date their integration has been marginal. In this article, we propose a collaborative model based on the social interactions that take place in a web-based conversational group recommender system. The collaborative model allows the group recommender to implicitly infer the different roles within the group, namely, collaborative and leader user(s). Moreover, it serves as the basis of several novel collaboration-based consensus strategies that integrate both individual and social interactions in the group recommendation process. A live-user evaluation confirms that our approach accurately identifies the collaborative and leader users in a group and produces more effective recommendations.
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Wang, Rui, and Guang Zhou Zeng. "A Dynamic Group Service Recommender System in Migrating Workflows." Applied Mechanics and Materials 16-19 (October 2009): 164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.164.

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Workflows provide the automation of business processes for streamline organization structure, increase efficiency, and reduce costs. In this paper, we propose a new scheme called Dynamic Group Service Recommendation (DGSR) which comprises group setup, joining group, group recommendation and verification phase. The simulation results demonstrate that DGSR significantly boosts the performance of active services. This group service recommender system is especially beneficial to concurrent transmissions of shared data in migrating workflows.
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Abolghasemi, Roza, Paal Engelstad, Enrique Herrera-Viedma, and Anis Yazidi. "A personality-aware group recommendation system based on pairwise preferences." Information Sciences 595 (May 2022): 1–17. http://dx.doi.org/10.1016/j.ins.2022.02.033.

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Alfitrassalam, Esa, Ade Romadhon, and Z. K. Abdurahman Baizal. "Group Recommender System Using Hybrid Method." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (October 26, 2021): 1217. http://dx.doi.org/10.30865/mib.v5i4.3220.

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In our daily activities, we make a lot of decisions either individually or in groups. The recommender systems is a solution for making decisions. One of the most common recommender systems is the recommendation for tourist destinations, where a number of tourist attractions are given as tourist attractions that are recommended to be visited by someone. There are still few recommended tourist attractions that provide recommendations for a group, while there are several tourist attractions that are more suitable if visited by several people at the same time. In this study, a recommender system for tourist attractions in Bandung-Raya Regency is proposed which is given to user groups. The recommended method used is Hybrid Collaborative Filtering and Knowledge-Based Filtering. In the process of selecting groups that are candidates to be recommended to users, Borda calculations are carried out with votes so that users can determine whether they like or dislike and match or not match the recommender generated by the system. The results of the evaluation of experiments conducted by taking surveys of users showed an average value. the average of the indicators of user satisfaction with the results of group recommender is 4.4 on the scale (1-5)
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Rani, Geeta, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, and Pradeep Kumar Tiwari. "Intelligent and Adaptive Web Page Recommender System." International Journal of Web Services Research 18, no. 4 (October 2021): 27–50. http://dx.doi.org/10.4018/ijwsr.2021100102.

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In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.
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Roy, Arup, Soumya Banerjee, Chintan Bhatt, Youakim Badr, and Saurav Mallik. "Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data." Journal of Information & Knowledge Management 17, no. 02 (June 2018): 1850019. http://dx.doi.org/10.1142/s0219649218500193.

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Since the introduction of Web 2.0, group recommendation systems become an effective tool for consulting and recommending items according to the choices of group of likeminded users. However, the population of dataset consisting of the large number of choices increases the size of storage. As a result, identification of the combination for specific recommendation becomes complex. Hence, the existing group recommendation system should support methodology for handling large data volume with varsity. In this paper, we propose a content-boosted modified termite colony optimisation-based rating prediction algorithm (CMTRP) for group recommendation system. CMTRP employs a hybrid recommendation framework with respect to the big data paradigm to deal with the trend of large data. The framework utilises the communal ratings that help to overcome the scalability problem. The experimental results reveal that CMTRP provides less error in the rating prediction and higher recommendation precision compared with the existing algorithms.
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Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Novel Rule based Data Mining Approach towards Movie Recommender System." Journal of information and organizational sciences 44, no. 1 (June 25, 2020): 157–70. http://dx.doi.org/10.31341/jios.44.1.7.

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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.
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Cho, Dong-Ju, Kee-Wook Rim, Jung-Hyun Lee, and Kyung-Yong Chung. "Method of Associative Group Using FP-Tree in Personalized Recommendation System." Journal of the Korea Contents Association 7, no. 10 (October 28, 2007): 19–26. http://dx.doi.org/10.5392/jkca.2007.7.10.019.

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Khazaei and Alimohammadi. "Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks." ISPRS International Journal of Geo-Information 8, no. 9 (September 12, 2019): 406. http://dx.doi.org/10.3390/ijgi8090406.

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Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.
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Otsuka, Yuichiro, Junshan Hu, and Tomoo Inoue. "Tabletop dish recommendation system for social dining: Group FDT design based on the investigations of dish recommendation." Journal of Information Processing 21, no. 1 (2013): 100–108. http://dx.doi.org/10.2197/ipsjjip.21.100.

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Xu, Sheng Wu, and Zheng You Xia. "Hot News Recommendation System across Heterogonous Websites Using Hadoop." Advanced Materials Research 989-994 (July 2014): 4704–7. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4704.

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The current most news recommendations are suitable for news which comes from a single news website, not for news from different news websites. Little research work has been reported on utilizing hundreds of news websites to provide top hot news services for group customers (e.g. Government staffs). In this paper, we present hot news recommendation system based on Hadoop, which is from hundreds of different news websites. We discuss our news recommendation system architecture based on Hadoop.We conclude that Hadoop is an excellent tool for web big data analytics and scales well with increasing data set size and the number of nodes in the cluster. Experimental results demonstrate the reliability and effectiveness of our method.
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Prayoga, Fajar Aji, and Kusnawi Kusnawi. "SMARTPHONE RECOMMENDATION SYSTEM USING MODEL-BASED COLLABORATIVE FILTERING METHOD." Jurnal Teknik Informatika (Jutif) 3, no. 6 (December 26, 2022): 1613–22. http://dx.doi.org/10.20884/1.jutif.2022.3.6.413.

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Smartphone are now an importan item that is needed by many people. The rapid development of technology make smartphone companies are competing to release their best smartphones.The many smartphones in online shop cause user to become disoriented about their choice. A recommendation system can help the user in choosing the smartphone that the user likes. In this study, a recommendation system was made using the collaborative filtering method with the K-Nearest Neighbors algorithm and combined with the application of K-Means algorithm to divide the smartphone into several group. The output of collaborative filtering method is that the model can give smartphone rating predictions to user. The prediction results will be used as the basis for giving recommendations to user. The purpose of smartphones groupping is so that the recommendation results are more specific and accurate. The evaluation of the model gets an MAE value is 1.1047 and RMSE value is 1.7579. So it can be concluded that the development of a smartphone recommendation system was successfully implemented.
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Yacouba, Kyelem, Kabore Kiswendsida Kisito, Ouedraogo TounwendyamFrédéric, and Sèdes Florence. "Recommendation Generation Justified for Information Access Assistance Service (IAAS) : Study of Architectural Approaches." International Journal of Computer Science and Information Technology 13, no. 6 (December 31, 2021): 1–17. http://dx.doi.org/10.5121/ijcsit.2021.13601.

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Recommendation systems only provide more specific recommendations to users. They do not consider giving a justification for the recommendation. However, the justification for the recommendation allows the user to make the decision whether or not to accept the recommendation. It also improves user satisfaction and the relevance of the recommended item. However, the IAAS recommendation system that uses advisories to make recommendations does not provide a justification for the recommendations. That is why in this article, our task consists for helping IAAS users to justify their recommendations. For this, we conducted a related work on architectures and approaches for justifying recommendations in order to identify an architecture and approach suitable for the context of IAAS. From the analysis in this article, we note that neither of these approaches uses the notices (IAAS mechanism) to justify their recommendations. Therefore, existing architectures cannot be used in the context of IAAS. That is why,we have developed a new IAAS architecture that deals separately with item filtration and justification extraction that accompanied the item during recommendation generation (Figure 7). And we haveimproved the reviews by adding users’ reviews on the items. The user’s notices include the Documentary Unit (DU), the user Group (G), the Justification (J) and the weight (a); noted A=(DU,G,J,a).
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Levus, Ye V., and R. B. Vasyliuk. "RECOMMENDATION ALGORITHM USING DATA CLUSTERING." Ukrainian Journal of Information Technology 4, no. 2 (2022): 18–24. http://dx.doi.org/10.23939/ujit2022.02.018.

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Recommender systems play a vital role in the marketing of various goods and services. Despite the intensive growth of the theory of recommendation algorithms and a large number of their implementations, many issues remain unresolved; in particular, scalability, quality of recommendations in conditions of sparse data, and cold start. A modified collaborative filtering algorithm based on data clustering with the dynamic determination of the number of clusters and initial centroids has been developed. Data clustering is performed using the k-means method and is applied to group similar users aimed at increase of the quality of the recommendation results. The number of clusters is calculated dynamically using the silhouette method, the determination of the initial centroids is not random, but relies on the number of clusters. This approach increases the performance of the recommender system and increases the accuracy of recommendations since the search for recommendations will be carried out within one cluster where all elements are already similar. Recommendation algorithms are software-implemented for the movie recommendation system. The software implementation of various methods that allow the user to receive a recommendation for a movie meeting their preferences is carried out: a modified algorithm, memory and neighborhood-based collaborative filtering methods. The results obtained for input data of 100, 500 and 2500 users under typical conditions, data sparsity and cold start were analyzed. The modified algorithm shows the best results – from 35 to 80 percent of recommendations that meet the user's expectations. The drop in the quality of recommendations for the modified algorithm is less than 10 per cent when the number of users increases from 100 to 2500, which indicates a good level of scalability of the developed solution. In the case of sparse data (40 percent of information is missing), the quality of recommendations is 60 percent. A low quality (35 percent) of recommendations was obtained in the case of a cold start – this case needs further investigation. Constructed algorithms can be used in rating recommender systems with the ability to calculate averaged scores for certain attributes. The modified recommendation algorithm is not tied to this subject area and can be integrated into other software systems.
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Alsobhi, Aisha, and Ngiste Amare. "Ontology-Based Relational Product Recommendation System." Computational and Mathematical Methods in Medicine 2022 (September 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1591044.

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As online shopping has expanded, product recommendations on e-commerce websites have gained significance. Systems for recommending products use information about site navigation and user leave-over to suggest more products. Customers who use a product recommendation system choose better and find items more quickly. On e-commerce websites, collaborative and content-based filtering is used in product suggestion algorithms. Collaborative filtering is driven by user preference similarity and content-based filtering. While content-based filtering groups are related to products, collaboration groups are like-minded individuals. In collaborative filtering, users with similar user profiles are used during the proposal phase; in content-based filtering, users with similar product profiles are found and recommended. These techniques cannot deliver complex commodities and have slow start-up times and small element sets. Users can push the same product if they only like certain things, but they cannot recommend a new product or user who just joined the system because they are not a group member. These approaches cannot capture complex semantic relationships, making them inadequate for recommending complex products. Recent research has focused on incorporating the domain ontology into the proposition process to create a more precise and helpful suggestion. The relational qualities of the product are not covered in this study, only its category and features are. Actually, the ontology of the proposed product should be included in the suggestion system. Relational data is integrated into the recommendation engine in this study using domain ontologies. This was done to research books that people had recommended. Relational data from an online bookseller was used to test the proposed infrastructure.
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Arun Raj Kumar, P., and Nagarajan Kumar. "STEM: STacked Ensemble Model design for aggregation technique in Group Recommendation System." International Journal of Business Intelligence and Data Mining 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbidm.2022.10037757.

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Kumar, Nagarajan, and P. Arun Raj Kumar. "STEM: stacked ensemble model design for aggregation technique in group recommendation system." International Journal of Business Intelligence and Data Mining 21, no. 1 (2022): 66. http://dx.doi.org/10.1504/ijbidm.2022.123809.

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Li, Hui, Jiangtao Cui, Bingqing Shen, and Jianfeng Ma. "An intelligent movie recommendation system through group-level sentiment analysis in microblogs." Neurocomputing 210 (October 2016): 164–73. http://dx.doi.org/10.1016/j.neucom.2015.09.134.

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Thompson, C. A., M. H. Goker, and P. Langley. "A Personalized System for Conversational Recommendations." Journal of Artificial Intelligence Research 21 (March 1, 2004): 393–428. http://dx.doi.org/10.1613/jair.1318.

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Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.
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Kaushik, Kartik. "Music Recommendation System using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 2682–88. http://dx.doi.org/10.22214/ijraset.2021.36946.

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Music рlаys аn imроrtаnt rоle in humаn lifestyles. Humans рrefers tо hear tо musiс/songs mоre оften thаn аbig apple оther pursuit. With internet teсhnоlоgies, large quantity оf musiс соntent hold musiс оf several genres hаs beсоme’s eаsily аccessible tо milliоns оf user аrоund whole wоrld. Musiс group sinсe deсаde аnd соmрgrowing оf many genres оf musiс is accessible. The mаjоr diffiсulties thаt customer fасe is tо choose аррrорriаte song/musiс frоm suсh big collection of music. The objective оf our рrоjeсt wаs tо reсоmmend sоngs tо customers built exclusively оn their listening habits, with nо knowledge аbоut the musiс. Musiс аррliсаtiоns аre аttemрting tо imрrоve their reсоmmendаtiоn structures in оrder tо оffer their customers the quality роssible listening exрerienсe аnd keeр them оn their рlаtfоrm. For better reсоmmendаtiоns, view аnаlysis will be рerfоrm оn the lyriсs оf sоng and the use of rаndоm-fоrest аlgоrithm will be use fоr сlаssified the song lines intо vаriоus саtegоry (hаррy, sаd).
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Xia, Zhengyou, Shengwu Xu, Ningzhong Liu, and Zhengkang Zhao. "Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/734351.

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The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.
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Wang, Xiangshi, Lei Su, Qihang Zhou, and Liping Wu. "Group Recommender Systems Based on Members’ Preference for Trusted Social Networks." Security and Communication Networks 2020 (May 19, 2020): 1–11. http://dx.doi.org/10.1155/2020/1924140.

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With the development of the Internet of Things (IoT), the group recommender system has also been extended to the field of IoT. The entities in the IoT are linked through social networks, which constitute massive amounts of data. In group activities such as group purchases and group tours, user groups often exhibit common interests and hobbies, and it is necessary to make recommendations for certain user groups. This idea constitutes the group recommender system. However, group members’ preferences are not fully considered in group recommendations, and how to use trusted social networks based on their preferences remains unclear. The focus of this paper is group recommendation based on an average strategy, where group members have preferential differences and use trusted social networks to correct for their preferences. Thus, the accuracy of the group recommender system in the IoT and big data environment is improved.
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Roy, Animesh Chandra, Mohammad Shamsul Arefin, A. S. M. Kayes, Mohammad Hammoudeh, and Khandakar Ahmed. "An Empirical Recommendation Framework to Support Location-Based Services." Future Internet 12, no. 9 (September 17, 2020): 154. http://dx.doi.org/10.3390/fi12090154.

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The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.
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Tian, Zhiqiang, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, and Mingyue Zhu. "Exploiting Group Information for Personalized Recommendation with Graph Neural Networks." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–23. http://dx.doi.org/10.1145/3464764.

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Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared
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40

Bagunaid, Wala, Naveen Chilamkurti, and Prakash Veeraraghavan. "AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data." Sustainability 14, no. 17 (August 24, 2022): 10551. http://dx.doi.org/10.3390/su141710551.

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Educational systems have advanced with the use of electronic learning (e-learning), which is a promising solution for long-distance learners. Students who engage in e-learning can access tests and exams online, making education more flexible and accessible. This work reports on the design of an e-learning system that makes recommendations to students to improve their learning. This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation. In addition, the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number. Individual scores are determined using a recurrent neural network (RNN) based on student engagement and examination scores. Then, a density-based spatial clustering algorithm (DBSCAN) using Mahalanobis distance clustering is implemented to group students based on their obtained score values. The constructed clusters are validated by estimating purity and entropy. Student performance is predicted using a threshold-based MapReduce (TMR) procedure from the score-based cluster. When predicting student performance, students are classified into two groups: average and poor, with the former being divided into below- and above-average students and the latter into poor and very poor students. This categorisation aims to provide useful recommendations for learning. A recommendation reinforcement learning algorithm, the rule-based state–action–reward–state–action (R-SARSA) algorithm, is incorporated for evaluation. Students were required to work on their subjects according to the provided recommendations. This e-learning recommendation system achieves better performance in terms of true-positives, false-positives, true-negatives, false-negatives, precision, recall, and accuracy.
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Zolkaflil, Salwa, Normah Omar, and Sharifah Nazatul Faiza Syed Mustapha Nazri. "Comprehensive cross-border declaration system as money-laundering prevention mechanism." Journal of Money Laundering Control 20, no. 3 (July 3, 2017): 292–300. http://dx.doi.org/10.1108/jmlc-02-2016-0008.

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Purpose This study aims to discuss the Financial Action Task Force (FATF) Special Recommendation IX (SR IX) and the importance of complying with the recommendation, which focuses on cross-border declaration or disclosure with the objective to detect and prevent illicit cross-border transportation of cash and bearer negotiable instruments (BNIs). This study also looks into compliance ratings of Asia Pacific Group (APG) 40 countries on the FATF SR IX. Design/methodology/approach This study reviews the mutual evaluation reports issued by APG on money laundering from 2006 to 2012. Based on the mutual evaluation reports, this study also looks into recommendations and comments given by respective panels. The compliance ratings together with panel’s recommendations and comments compiled in this study will be helpful to relevant authorities for future improvement. Findings Complying to FATF SR IX helps relevant authorities in detecting and preventing illicit from cross-border transportation of cash and BNIs. Out of 40, only two countries received compliant rating, which shows the need of improvement to ensure that the country is compliant on FATF SR IX. Research limitations/implications This study is limited to the panel’s reviews and recommendations on mutual evaluation report and only focuses on FATF SR IX. Originality/value This paper analyzes the compliance characteristics of countries based on their FATF mutual evaluation report. It highlights the comments and recommendation for future improvement to ensure that these countries will comply with FATF SR IX.
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Park, Han-Saem, Moon-Hee Park, and Sung-Bae Cho. "Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network." International Journal of Information Technology & Decision Making 14, no. 02 (March 2015): 317–38. http://dx.doi.org/10.1142/s0219622015500017.

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The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process of multi-criteria decision-making method is used to estimate the group users' preference from individual users' preferences. Experiments in 10 different situations provide a comparison of the proposed method with random recommendation, simple rule-based recommendation and neural network recommendation, and confirm that the proposed method is useful with the subjective test.
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GRIFFITH, JOSEPHINE, COLM O'RIORDAN, and HUMPHREY SORENSEN. "IDENTIFYING USER AND GROUP INFORMATION FROM COLLABORATIVE FILTERING DATASETS." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 02 (March 2007): 291–310. http://dx.doi.org/10.1142/s0218001407005405.

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This paper considers the information that can be captured about users and groups from a collaborative filtering dataset. The aims of the paper are to create a user model and to use this model to explain the performance of a collaborative filtering approach. A number of user and group features are defined and the performance of a collaborative filtering system in producing recommendations for users with different feature values is tested. Graph-based representations of the collaborative filtering space are presented and these are used to define some of the user and group features as well as being used in a recommendation task.
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Chen, Hung-Kai, Fueng-Ho Chen, and Shien-Fong Lin. "An AI-Based Exercise Prescription Recommendation System." Applied Sciences 11, no. 6 (March 16, 2021): 2661. http://dx.doi.org/10.3390/app11062661.

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The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs.
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Al-Ballaa, Hailah, Hmood Al-Dossari, and Azeddine Chikh. "Using an Exponential Random Graph Model to Recommend Academic Collaborators." Information 10, no. 6 (June 25, 2019): 220. http://dx.doi.org/10.3390/info10060220.

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Academic collaboration networks can be formed by grouping different faculty members into a single group. Grouping these faculty members together is a complex process that involves searching multiple web pages in order to collect and analyze information, and establishing new connections among prospective collaborators. A recommender system (RS) for academic collaborations can help reduce the time and effort required to establish a new collaboration. Content-based recommendation system make recommendations based on similarity without taking social context into consideration. Hybrid recommender systems can be used to combine similarity and social context. In this paper, we propose a weighting method that can be used to combine two or more social context factors in a recommendation engine that leverages an exponential random graph model (ERGM) based on historical network data. We demonstrate our approach using real data from collaborations with faculty members at the College of Computer and Information Sciences (CCIS) in Saudi Arabia. Our results demonstrate that weighting social context factors helps increase recommendation accuracy for new users.
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Dong, Jie, Gui Li, Wenkai Ma, and Jianshun Liu. "Personalized recommendation system based on social tags in the era of Internet of Things." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 681–89. http://dx.doi.org/10.1515/jisys-2022-0053.

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Abstract With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the influence of continuous user behavior, in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of <user-tag-item> into two two-dimensional relationships of <user-tag> and <tag-item>. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms.
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Geng, Li. "The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model." Computational Intelligence and Neuroscience 2022 (June 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/7228833.

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In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to “information trek.” Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities.
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Liu, Yezheng, Lu Yang, Jianshan Sun, Yuanchun Jiang, and Jinkun Wang. "Collaborative matrix factorization mechanism for group recommendation in big data-based library systems." Library Hi Tech 36, no. 3 (September 17, 2018): 458–81. http://dx.doi.org/10.1108/lht-06-2017-0121.

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Purpose Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.
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Wang, Qin. "The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage." Advances in Multimedia 2022 (October 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/5811341.

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With the accelerated trend of globalization and modernization, many provincial intangible cultural heritages (PICH) are in danger of being lost. In the context of Internet technology, the use of digital multimedia for personalized recommendation is an effective way to promote the transmission of intangible cultural heritage. However, traditional recommendation systems tend to treat different members as homogeneous objects, ignoring the relationship between members’ professional backgrounds and the inherent properties of items, and cannot truly solve the problem of conflicting preferences in the integration process. In view of this, this paper proposes a group recommendation system based on nonnegative matrix decomposition. First, the group rating information is decomposed into a user matrix and item matrix by nonnegative matrix decomposition. Then, the item affiliation matrix and member expertise matrix are calculated by using the affiliation and expertise weights for the two matrices, respectively, and the contribution degree of each member to different item ratings is obtained from them. Finally, the group preference model is constructed by weighted fusion of members’ preferences based on their contribution degrees, and different recommendation lists are generated for different user preferences. The experimental results prove that this system has high recommendation accuracy in cross-regional promotion of PICH.
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Li, Guangli, Jin Hua, Tian Yuan, Jinpeng Wu, Ziliang Jiang, Hongbin Zhang, and Tao Li. "Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++." Mathematical Problems in Engineering 2019 (July 17, 2019): 1–15. http://dx.doi.org/10.1155/2019/2072375.

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Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named “Smart Travel” is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named LA is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named LB is generated in turn on the basis of rating predictions. Finally, the two lists LA and LB are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.
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