Journal articles: 'Content recommendations'

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Lidén, Erik R. "Swedish Stock Recommendations: Information Content or Price Pressure?" Multinational Finance Journal 11, no. 3/4 (December 1, 2007): 253–85. http://dx.doi.org/10.17578/11-3/4-4.

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Han, Jonghyun, Hedda R. Schmidtke, Xing Xie, and Woontack Woo. "Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity." Pervasive and Mobile Computing 13 (August 2014): 85–98. http://dx.doi.org/10.1016/j.pmcj.2013.11.002.

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Yoo, Youngtae, and Hyunjun Park. "The Informational Content Of Changes In Stock Recommendation: Chaebol Vs. Non-Chaebol Affiliated Analysts." Journal of Applied Business Research (JABR) 32, no. 6 (November 2, 2016): 1687. http://dx.doi.org/10.19030/jabr.v32i6.9816.

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Accurate analysts’ reports alleviate information asymmetry between companies and investors by providing accounting information that is useful in investment decision-making for market participants. Investors evaluate the credibility of stock recommendations based on the accuracy of the earnings forecasts of analysts, applying them in the decision-making process. Studies of stock recommendations have focused on their informational content, systematically analyzing the characteristics of recommendations and, to a lesser degree, decision-making factors. For most analysts, when stock recommendations and forecast changes are simultaneously disclosed, a large bias results if analysts fail to consider the magnitude of the market reaction relative to the earnings forecast and stock recommendations. In most previous studies, the informational content of both individual stock recommendations and changes in stock recommendations was investigated. In this study, we examine differences in the informational content depending on the stock recommendations of the report released immediately previous to the current report for the same recommendation. An upgraded (or downgraded) revision within the same recommendation category is associated with a greater (lower) stock price return. Even the same recommendation in the market may cause different reactions depending on both the recommendation itself and on the direction of change of the recommendation. Affiliated analysts have more access to inside information of the companies they analyze. The stock returns after revisions of Chaebol-affiliated analysts are significantly higher than those of non-Chaebol-affiliated analysts.

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Jabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.

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Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

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Varada, Sowmya, Ronilda Lacson, Ali S. Raja, Ivan K. Ip, Louise Schneider, David Osterbur, Paul Bain, et al. "Characteristics of knowledge content in a curated online evidence library." Journal of the American Medical Informatics Association 25, no. 5 (October 27, 2017): 507–14. http://dx.doi.org/10.1093/jamia/ocx092.

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Abstract Objective To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. Materials and Methods The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. Results The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were “single-decision,” “branching,” and “score-based.” Discussion Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. Conclusion In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.

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Afolabi, Ibukun Tolulope, Opeyemi Samuel Makinde, and Olufunke Oyejoke Oladipupo. "Semantic Web mining for Content-Based Online Shopping Recommender Systems." International Journal of Intelligent Information Technologies 15, no. 4 (October 2019): 41–56. http://dx.doi.org/10.4018/ijiit.2019100103.

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Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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Javed, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Sivanaiah, Rajalakshmi, R. Sakaya Milton, and T. T. Mirnalinee. "Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1477–96. http://dx.doi.org/10.3233/ida-205244.

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The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.

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Aloudat, M., A. Papp, N. Magyar, L. Simon Sarkadi, and A. Lugasi. "Nutritional Value of Traditional and Modern Meals: Jordan and Hungary." Acta Alimentaria 49, no. 4 (November 7, 2020): 491–97. http://dx.doi.org/10.1556/066.2020.49.4.15.

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The purpose of this study was to compare the energy content and macronutrients of forty main popular traditional and modern meals in both Jordan and Hungary with the national and international recommendations. The calculation of energy content and macronutrients were done on traditional and modern recipes by two diﬀerent softwares (ESHA and NutriComp). Neither Jordanian nor Hungarian foods met the recommended energy content (35% of daily energy intake, 8400 kJ for energy intake). The recipes of both nations are characterised by higher protein, fat, and salt contents than WHO recommendation, a lower ﬁbre content, and sugar content within the recommended limits. The fat energy ratio and saturated fatty acid content of Hungarian recipes are signiﬁcantly higher than WHO recommendation. In general, Jordanian meals were more likely to meet the inclusion criteria. In conclusion, neither Jordanian nor Hungarian traditional and popular meals meet the international nutritional recommendations for a healthy diet, however, the composition of the real dishes may diﬀer signiﬁcantly from the recipes depending on the available ingredients and chosen kitchen technology.

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Zadro, Joshua, Aimie L. Peek, Rachael H. Dodd, Kirsten McCaffery, and Christopher Maher. "Physiotherapists’ views on the Australian Physiotherapy Association’s Choosing Wisely recommendations: a content analysis." BMJ Open 9, no. 9 (September 2019): e031360. http://dx.doi.org/10.1136/bmjopen-2019-031360.

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ObjectivesChoosing Wisely holds promise for increasing awareness of low-value care in physiotherapy. However, it is unclear how physiotherapists’ view Choosing Wisely recommendations. The aim of this study was to evaluate physiotherapists’ feedback on Choosing Wisely recommendations and investigate agreement with each recommendation.SettingThe Australian Physiotherapy Association emailed a survey to all 20 029 physiotherapist members in 2015 seeking feedback on a list of Choosing Wisely recommendations.ParticipantsA total of 9764 physiotherapists opened the email invitation (49%) and 543 completed the survey (response rate 5.6%). Participants were asked about the acceptability of the wording of recommendations using a closed (Yes/No) and free-text response option (section 1). Then using a similar response format, participants were asked whether they agreed with each Choosing Wisely recommendation (sections 2–6).Primary and secondary outcomesWe performed a content analysis of free-text responses (primary outcome) and used descriptive statistics to report agreement and disagreement with each recommendation (secondary outcome).ResultsThere were 872 free-text responses across the six sections. A total of 347 physiotherapists (63.9%) agreed with the ‘don’t’ style of wording. Agreement with recommendations ranged from 52.3% (electrotherapy for back pain) to 76.6% (validated decision rules for imaging). The content analysis revealed that physiotherapists felt that blanket rules were inappropriate (range across recommendations: 13.9%–30.1% of responses), clinical experience is more valuable than evidence (11.7%–28.3%) and recommendations would benefit from further refining or better defining key terms (7.3%–22.4%).ConclusionsAlthough most physiotherapists agreed with both the style of wording for Choosing Wisely recommendations and with the recommendations, their feedback highlighted a number of areas of disagreement and suggestions for improvement. These findings will support the development of future recommendations and are the first step towards increasing the impact Choosing Wisely has on physiotherapy practice.

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Brenneman, James A. "Dietary Recommendations and Nutrient Content of Food." American Biology Teacher 50, no. 2 (February 1, 1988): 114–17. http://dx.doi.org/10.2307/4448659.

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Fedorenko, V. I., and V. S. Kireev. "TEXT EMBEDDINGS FOR CONTENT-BASED RECOMMENDATIONS AUTHORS." Современные наукоемкие технологии (Modern High Technologies), no. 3 2018 (2018): 102–6. http://dx.doi.org/10.17513/snt.36944.

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Kobal, Michael. "The Predictive Content of Aggregate Analyst Recommendations." CFA Digest 39, no. 4 (November 2009): 57–58. http://dx.doi.org/10.2469/dig.v39.n4.070.p21.

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HOWE, JOHN S., EMRE UNLU, and XUEMIN (STERLING) YAN. "The Predictive Content of Aggregate Analyst Recommendations." Journal of Accounting Research 47, no. 3 (June 2009): 799–821. http://dx.doi.org/10.1111/j.1475-679x.2009.00337.x.

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Meghana, K., E. Sudeeksha, A. Somanth, and Dr Y. Srinivasulu. "Content Based Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 992–96. http://dx.doi.org/10.22214/ijraset.2023.48533.

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Abstract: Recommender System is a tool which helps users find the required content and overcome information overload. It predicts interests of users by using Machine Learning algorithms and makes recommendation according to the interest of users. The primary content-based recommender system is the continuation and development of collaborative filtering, which does not need the user’s appraisal for items. Instead, the similarity is calculated based on the data of items that are selected by users, and then make the recommendation appropriately. With the augmentation of machine learning, the current content-based recommender system can build profile for users and products respectively. Building or renewing the profile according to the perusal of items that are bought or seen by users. The system can differentiate the user and the profile of items and then recommend the most resembling products. So, this recommender method that compel user and product directly can’t be brought into collaborative filtering model. The groundwork of content-based algorithm is acquisition and quantitative analysis of the content. The research of acquisition and filtering of text information are fully fledged, many current modified content-based recommender systems make recommendations according to the analysis of text data. This paper introduces content-based recommendation system for the movie websites. There are a lot of factors extracted from the movie, they are diverse and unique, which is also different from other recommender systems. We use these aspects to construct movie model and calculate similarity. We introduce a new outlook for setting weight of features, which improvises the representation of movie recommendations. Finally, we evaluate the approach to illustrate the improvement

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Strek, H. J., J. J. Dulka, and A. J. Parsells. "Humic matter content vs. organic matter content for making herbicide recommendations." Communications in Soil Science and Plant Analysis 21, no. 13-16 (August 1990): 1985–95. http://dx.doi.org/10.1080/00103629009368352.

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Chakraborty, Abhijnan, Johnnatan Messias, Fabricio Benevenuto, Saptarshi Ghosh, Niloy Ganguly, and Krishna Gummadi. "Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (May 3, 2017): 22–31. http://dx.doi.org/10.1609/icwsm.v11i1.14894.

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Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (for example, Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service Who-Makes-Trends at twitter-app.mpi-sws.org/who-makes-trends.

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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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Bharadwaj, Brijgopal, Ramani Selvanambi, Marimuthu Karuppiah, and Ramesh Chandra Poonia. "Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning." International Journal of Social Ecology and Sustainable Development 13, no. 9 (January 2022): 1–18. http://dx.doi.org/10.4018/ijsesd.292053.

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This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various considered content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences, by assuming the environment’s reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and at-par with the user satisfaction, within the bounds of the input data capabilities.

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Hempel, Susanne, Isomi Miake-Lye, Angela G. Brega, Fred Buckhold, Susan Hassell, Mary Patricia Nowalk, Lisa Rubenstein, et al. "Quality Improvement Toolkits: Recommendations for Development." American Journal of Medical Quality 34, no. 6 (January 24, 2019): 538–44. http://dx.doi.org/10.1177/1062860618822102.

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A burgeoning number of toolkits dedicated to improving health care exist but development guidance is lacking. The authors convened a panel of health care stakeholders, including developers, purchasers, users, funders, and disseminators of toolkits. The panel was informed by a literature review that analyzed 44 publications and 27 toolkits. A modified Delphi process established recommendations and suggestions to guide toolkit development. The panel established 12 recommendations for content and 1 recommendation for toolkit development methods. The recommendations are accompanied by 11 suggestions for toolkit content, 9 suggestions for development methods, and 6 suggestions for toolkit evaluation methods. The authors established a set of key recommendations and suggestions addressing the content, development, and evaluation methods of quality improvement toolkits, together with a ready-to use checklist. The guidance aims to advance the value of toolkits as an emerging method to effectively disseminate interventions to improve the quality of care.

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Ikram, Fasiha, and Humera Farooq. "Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features." Scientific Programming 2022 (February 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/6084363.

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The increase of multimedia content in e-commerce and entertainment services creates a new research gap in the field of recommendation systems. The main emphasis of the presented work is on increasing the accuracy of multimedia recommendations using visual semantic content. Recent approaches have shown that the inclusion of visual information is helpful to understand the semantic features for a recommendation model. The researchers have contributed to the field of multimedia item recommendations using low-level visual semantic features. Here, we seek to extend this contribution by exploring the high-level visual semantic content using constant visual attributes for video game recommendation systems. With the exponential growth of multimedia content in the video game industry in the last decade, researchers investigate the importance of personalized video game recommendation techniques. Previous methods have not investigated the importance of visual semantic content for video game recommendations. A practical recommendation system for video games is challenging due to the data diversity, level of user interest, and semantic complexity of features involved. This study proposed a novel method named Deep Visual Semantic Multimedia Recommendation Systems (D_VSMR) to deal with high-level visual features for multimedia recommendation systems. A visual semantic-based video game recommendation system utilizing deep learning methods for visual content learning and user profile learning is introduced. The proposed approach employs content-based techniques to expand users’ profiles. The user profile expansion is based on the visual content of games. The required datasets have been obtained from video game e-commerce platforms like Google Play Store and Amazon for evaluation purposes. The evaluation results have shown that the proposed approach’s accuracy and effectiveness have been improved up to 95.87% compared to the other state-of-the-art methods.

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Fang, Bing, Enpeng Hu, Junyang Shen, Jingwen Zhang, and Yang Chen. "Implicit Feedback Recommendation Method Based on User-Generated Content." Scientific Programming 2021 (October 28, 2021): 1–15. http://dx.doi.org/10.1155/2021/3982270.

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Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

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Cromwell, Julia C., and Theodore A. Stern. "Publishing Case Reports: Educational Strategies and Content Recommendations." Psychosomatics 60, no. 4 (July 2019): 361–64. http://dx.doi.org/10.1016/j.psym.2019.02.009.

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Peska, Ladislav. "Hybrid recommendations by content-aligned Bayesian personalized ranking." New Review of Hypermedia and Multimedia 24, no. 2 (April 3, 2018): 88–109. http://dx.doi.org/10.1080/13614568.2018.1489002.

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Gohzali, Hernawati, Sunario Megawan, Erwin Erwin, and Jeffrey Onggo. "Rekomendasi Buku Menggunakan K-Nearest Neighbor (KNN) dan Binary Particle Swarm Optimization (BPSO)." Jurnal SIFO Mikroskil 20, no. 1 (April 4, 2019): 81–92. http://dx.doi.org/10.55601/jsm.v20i1.659.

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Most of recommender systems are based on content can be helpful to find recommendation book suitable for reader but it only consider about a liked book by user without considered about the disliked one. For solving the problem, a recommendation based on content of liked and dislike book by user is done. In this research, we applied Binary Particle Swarm Optimization(BPSO) to select feature from book that the reader like and K-Nearest Neighbor(KNN) are use for classify book data which had the closest distance to the book that the reader liked and disliked. Testing the accuracy of the recommendations is done by comparing the results of recommendations in the book data that do not apply feature selection with book data that applies selective features to test the effect of application of feature selection on book recommendations. The result of book recommendation accuracy testing with feature selection give a better recommendation for user than recommendation without feature selection.

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Chughtai, M. Waseem, Imran Ghani, Ali Selamat, and Seung Ryul Jeong. "Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios." International Journal of Technology and Educational Marketing 4, no. 1 (January 2014): 1–14. http://dx.doi.org/10.4018/ijtem.2014010101.

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Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

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Wibowotomo, Budi, Eris Dwi Septiawan Rizal, Muhammad Iqbal Akbar, and Dediek Tri Kurniawan. "Cooking Class Recommendation Using Content Based Filtering for Improving Chef Learning Practical Skill." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 08 (April 23, 2021): 71. http://dx.doi.org/10.3991/ijim.v15i08.21581.

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Koolinera is a web-based e-learning application about learning to cook Indonesian culinary dishes. Users are free to choose cooking classes. Culinary in Indonesia is very diverse, so many users feel confused in choosing a cooking class. No specific guidance is given to users on tips for choosing a cooking class. Therefore, it is important to develop a feature that can help users to guide the selection of cooking classes, namely by building a cooking class selection recommendation system. Class recommendations are obtained based on the last class taken by the user. The criteria used to determine the recommendations are the similarity of class names, dominant taste of cuisine, category of cuisine, area of origin, and tutor. The algorithm used is Content-Based Filtering with TF-IDF calculations. The recommendations given to users are a list of six cooking classes. Testing is carried out based on black box testing, expert validation, and user testing. The blackbox test carried out states that all functions are running well. The validity test of the media by the validator got a percentage of 96.52%. User testing in the Usability Tetsing Experience section got a percentage of 85.73%, User Acceptance Testing got a percentage of 83.89% and testing the relevance of the recommendation system got a percentage of 88.69%

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Ngqangashe, Yandisa, Charlotte de Backer, Christophe Matthys, and Nina Hermans. "Investigating the nutrient content of food prepared in popular children’s TV cooking shows." British Food Journal 120, no. 9 (September 3, 2018): 2102–15. http://dx.doi.org/10.1108/bfj-02-2018-0121.

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Purpose The purpose of this paper is to analyse the nutritional content of recipes prepared in popular children’s television (TV) cooking shows. Design/methodology/approach A cross-sectional analysis of 150 recipes focusing on calorie, total fat and carbohydrates, saturated fatty acids, fibre, sugar, protein and salt content was performed. Main course recipes were evaluated against the UK Food Standards Agency (FSA), and the proportions of energy derived from each nutrient were evaluated against the World Health Organization (WHO) recommendations. Findings While a significant proportion met the FSA and WHO recommendations for energy and salt, 58 per cent were above the FSA recommendation for total fat (χ2=5.598, p=0.01), 56 per cent failed to meet the recommendations for saturated fatty acids (χ2=4.551, p=0.03) and 60 per cent exceeded the FSA protein recommendations (χ2=12.602, p<0.001). Only 17 and 21 per cent of the recipes met the minimum recommendations for carbohydrates (χ2=30.429, p<0.001) and fibre (χ2=16.909, p<0.001), respectively. Only 37 per cent had adequate portion of fruits and vegetables. The nutritional content varied depending on the composition of the recipes; vegetarian recipes were more likely to meet the recommendations than poultry, meat or fish recipes. Research limitations/implications Foods displayed by children’s popular TV cooking show fall short of the standards for healthy eating, thus warranting further research on how these shows affect eating behaviour. Originality/value This study is the first to consider children’s TV cooking shows as a platform of exposure to unhealthy foods.

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Chinivar, Spoorthi. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (September 30, 2022): 105–9. http://dx.doi.org/10.35940/ijrte.c7274.0911322.

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Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method for content-based filtering, and deep learning for a collaborative approach, this study compares two movie recommendation system. The proposed systems are evaluated by calculating the precision and recall values. On a small dataset, a content-based filtering methodology had a precision of 5.6% whereas a collaborative approach had a precision of 57%. Collaborative filtering clearly worked better than content-based filtering. Future improvements involve creating a single hybrid recommendation system that combines a collaborative and content-based approach to improve the outcomes.

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Uçar, Tamer, and Adem Karahoca. "Personalizing trip recommendations: A framework proposal." Global Journal of Computer Science 5, no. 1 (November 13, 2015): 24. http://dx.doi.org/10.18844/gjcs.v5i1.30.

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Personalized trip planning is a very common problem in tourism domain. There are several studies in this area each one of all aims to provide recommendations based on user preferences. Recommendation engines mostly use two common methods: content based filtering and collaborative filtering. As a combination of these two methods, hybrid approaches are also popular for recommendation systems. This study provides a deep analysis about recent studies in trip recommendation domain. Applied techniques and mentioned methodologies in literature is discussed at all points. Insights about the proposed systems are provided clearly. Besides a literature survey, this study also proposes a novel travel recommender method based on a tourism datasource. A hybrid approach involving demographic, content-based and collaborative filtering techniques are proposed in order to eliminate drawbacks of each approach. Recommendations will be based on many factors including users’ demographic information, past travel locations and favorite seasons. Based on such inputs, recommender engine predicts possible travel locations along with various flight options. Possible challenges and future trends are concluded as a result of this study. Keywords: Recommender systems, trip recommendation, personalized recommendation, information filtering.

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Kupriyanov, Roman B., Dmitry L. Agranat, and Ruslan S. Suleymanov. "Use of artificial intelligence technologies for building individual educational trajectories of students." RUDN Journal of Informatization in Education 18, no. 1 (December 15, 2021): 27–35. http://dx.doi.org/10.22363/2312-8631-2021-18-1-27-35.

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Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

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Giannakas, Theodoros, Anastasios Giovanidis, and Thrasyvoulos Spyropoulos. "MDP-based Network Friendly Recommendations." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 4 (December 31, 2021): 1–29. http://dx.doi.org/10.1145/3513131.

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Controlling the network cost by delivering popular content to users, as well as improving streaming quality and overall user experience, have been key goals for content providers (CP) in recent years. While proposals to improve performance, through caching or other mechanisms (DASH, multicasting, etc.) abound, recent works have proposed to turn the problem on its head and complement such efforts. Instead of trying to reduce the cost to deliver every possible content to a user, a potentially very expensive endeavour, one could leverage omnipresent recommendations systems to nudge users towards the content of low(er) network cost, regardless of where this cost is coming from. In this paper, we focus on this latter problem, namely optimal policies for “Network Friendly Recommendations” (NFR). A key contribution is the use of a Markov Decision Process (MDP) framework that offers significant advantages, compared to existing works, in terms of both modeling flexibility as well as computational efficiency. Specifically we show that this framework subsumes some state-of-the-art approaches, and can also optimally tackle additional, more sophisticated setups. We validate our findings with real traces that suggest up to almost 2X in cost performance, and 10X in computational speed-up compared to recent state-of-the-art works.

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Riforgiate, Sarah, Ali Gattoni, and Erika Kirby. "Organizing the Organizational Communication Course: Content and Pedagogical Recommendations." Journal of Communication Pedagogy 2 (2019): 7–11. http://dx.doi.org/10.31446/jcp.2019.03.

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Organizational communication extends beyond communication that takes place in an organizational context to the ways communication is used to organize and facilitate activity. This article is designed to enhance organizational communication pedagogy practices by highlighting foundational concepts and content areas that should be included in undergraduate organizational communication courses. Additionally, four active learning assignments, including case studies, applied organizational communication theory papers, organizational audits, and media assignments, are described to enhance student engagement with class material and to assess student learning. Finally, the article includes common issues to help educators anticipate concerns and plan effective classroom strategies.

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Gordillo, Aldo, Daniel López-Fernández, and Katrien Verbert. "Examining the Usefulness of Quality Scores for Generating Learning Object Recommendations in Repositories of Open Educational Resources." Applied Sciences 10, no. 13 (July 4, 2020): 4638. http://dx.doi.org/10.3390/app10134638.

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Open educational resources (OER) can contribute to democratize education by providing effective learning experiences with lower costs. Nevertheless, the massive amount of resources currently available in OER repositories makes it difficult for teachers and learners to find relevant and high-quality content, which is hindering OER use and adoption. Recommender systems that use data related to the pedagogical quality of the OER can help to overcome this problem. However, studies analyzing the usefulness of these data for generating OER recommendations are very limited and inconclusive. This article examines the usefulness of using pedagogical quality scores for generating OER recommendations in OER repositories by means of a user study that compares the following four different recommendation approaches: a traditional content-based recommendation technique, a quality-based non-personalized recommendation technique, a hybrid approach that combines the two previous techniques, and random recommendations. This user study involved 53 participants and 400 OER whose quality was evaluated by reviewers using the Learning Object Review Instrument (LORI). The main finding of this study is that pedagogical quality scores can enhance traditional content-based OER recommender systems by allowing them to recommend OER with more quality without detriment to relevance.

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Yeruva, Sagar, Addi Sathvika, Damera Sruthi, Duggasani Yaswanth Reddy, and Gogineni Gopi Krishna. "Apparel Recommendation System using Content-Based Filtering." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 4 (November 30, 2022): 46–51. http://dx.doi.org/10.35940/ijrte.d7331.1111422.

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Nowadays, people are constantly moving towards various fashion products as a result the e-commerce market for garments is growing rapidly. Online stores must update their features according to user requirements and preferences. However, there are too many options for users to select from these online stores which may leave them in a dilemma to identify the correct outfit, save the user time, and increase sales, efficient recommendation systems are becoming a necessity for online retailers. In this paper, we proposed an Apparel Recommendation System that generates recommendations for users based on their input. We used a real-world data set taken from the online market giant Amazon using Amazon’s Product Advertising API. We aim to use keywords like brand, color, size, etc., to recommend. Data exploration to get detailed information about our dataset, Data Cleaning(pre-processing) to remove invalid sections, Model selection (We have compared different feature extraction techniques like bag of words, TF-IDF, and word2vec model) to find out efficient techniques and Deployment of the model that could facilitate recommendation system to simplify the task of apparel recommendation system. The accuracy of the model is identified using the response time and content matching.

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Bin Jamil, Haris, Aisha Ghazi Aurakzai, and Muhammad Subayyal. "Can Analysts Really Forecast? Evidence from the Karachi Stock Exchange." LAHORE JOURNAL OF ECONOMICS 19, no. 1 (January 1, 2014): 91–109. http://dx.doi.org/10.35536/lje.2014.v19.i1.a4.

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This study examines the impact of analysts’ recommendations on stock prices listed on the Karachi Stock Exchange for the period 2006–12. The recommendations are extracted from the daily Morning Shout report published by Khadim Ali Shah Bukhari Securities Ltd (KASB), which provides buy and sell recommendations for different stocks. We use the market model to estimate the abnormal returns around the recommendation dates for these securities. The study also investigates whether the abnormal returns are due to price pressure or information content. We find that investors earn abnormal returns on the basis of analysts’ recommendations for these securities. The results are robust in considering only the sub-sample subsequent to 2008’s global financial crisis, and are also consistent with the information content hypothesis and price pressure hypothesis.

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Drisko, James W. "Teaching qualitative research: Key content, course structures, and recommendations." Qualitative Social Work: Research and Practice 15, no. 3 (December 6, 2015): 307–21. http://dx.doi.org/10.1177/1473325015617522.

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Premti, Arjan, Luis Garcia-Feijoo, and Jeff Madura. "Information content of analyst recommendations in the banking industry." International Review of Financial Analysis 49 (January 2017): 35–47. http://dx.doi.org/10.1016/j.irfa.2016.11.005.

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Gupta, Samarth, and Sharayu Moharir. "Effect of Recommendations on Serving Content with Unknown Demand." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 4, no. 1 (March 3, 2019): 1–22. http://dx.doi.org/10.1145/3289324.

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Krippendorff, K. "Reliability in Content Analysis: Some Common Misconceptions and Recommendations." Human Communication Research 30, no. 3 (July 1, 2004): 411–33. http://dx.doi.org/10.1093/hcr/30.3.411.

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Bielecki, Jacek, Oskar Ceglarski, and Maria Skublewska-Paszkowska. "Machine Learning as a method of adapting offers to the clients." Journal of Computer Sciences Institute 13 (December 30, 2019): 267–71. http://dx.doi.org/10.35784/jcsi.1293.

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Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

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Nadeem, Rashid, and T. Sivakumar. "A Systematic Literature Survey on Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 1477–86. http://dx.doi.org/10.22214/ijraset.2023.48828.

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Abstract: For many applications, particularly in the academic environment and industry, the Recommendation System for Technical Paper Reviewers is very important. This study examines the research trends connecting the highly technical components of recommendation systems employed in various service fields to their commercial aspects. It is a technique that enables the user to identify the information that will be useful to him or her from the variety of facts accessible. In terms of the movie recommendation system, recommendations are made either based on user similarities in collaborative filtering or by considering the user's intended engagement with the content into account content-based filtering. A stronger recommendation system is produced by combining content-based and collaborative filtering, which overcomes the issues that collaborative and content-based filtering typically have. The similarity between users is also determined using a variety of similarity measures in order to make recommendations. We have reviewed cutting-edge approaches to collaborative filtering, content-based filtering, deep learning-based methods, and hybrid approaches in this study for movie recommendation. Additionally, we looked at other similarity measures. Numerous businesses, including Facebook, which suggests friends, LinkedIn, which suggests jobs, Pandora, which suggests music, Netflix, which suggests movies, and Amazon, which suggests purchases, among others, employ recommendation systems to boost their profits and help their clients. This essay primarily focuses on providing a succinct overview of the many approaches and techniques used for movie recommendation in order to investigate the field of recommendation systems research.

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Kesuma, Rahman Indra, and Amirul Iqbal. "Penerapan Content-Boosted Collaborative Filtering untuk Meningkatkan Kemampuan Sistem Rekomendasi Penyedia Jasa Acara Pernikahan." Jurnal Ilmiah FIFO 12, no. 1 (May 1, 2020): 112. http://dx.doi.org/10.22441/fifo.2020.v12i1.009.

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AbstractThe changes in lifestyle of the global society in the era of digital world development have made the smartphone technology penetration to rise continually. This condition can increase business opportunities, especially e-commerce activities that utilize technology and the internet in terms of promotions and transactions. The efficiency and effectiveness is an interesting focus that is discussed in this issue. For example, in services or products searching for a wedding where many customers still feel difficult and need a long time to find the desired things. The existence of a recommendation system also has not been able to help, especially for users who are newly registered to the system. This is because most of them will provide recommendations based on a history of user activity. Therefore, this study applies the content-boosted collaborative filtering (CBCF) method to improve the ability of the recommendation system in providing recommendations for weddings, especially for a new user. The obtained results are then compared with two commonly used methods, content-based recommendations (CB) and collaborative filtering (CF). Based on the experimental results, it can be concluded that CBCF can maintain the quality of good recommendations for long registered users with an accuracy of 84% and also can provide recommendations for new users with an accuracy of 54% which is cannot be solved by CB or CF methods.Key Word: digital businesses, wedding vendors/organizers, recommendation system, content-boosted collaborative filtering AbstrakPerubahan pola kehidupan masyarakat global pada era perkembangan dunia digital membuat penetrasi dari teknologi telepon pintar terus menaik. Kondisi ini dapat meningkatkan kesempatan bisnis khususnya kegiatan jual beli yang memanfaatkan teknologi dan internet dalam hal promosi dan transaksi. Efisiensi dan efektifitas proses menjadi fokus yang terus menarik dibahas dalam hal ini. Sebagai contoh, pada pencarian layanan atau produk untuk pernikahan yang mana banyak pelanggan masih merasakan kesulitan dan membutuhkan waktu yang lama untuk mencari sesuatu yang diinginkannya. Keberadaan sistem rekomendasi juga belum bisa membantu terlebih bagi pengguna yang baru terdaftar pada sistem. Hal ini dikarenakan kebanyakan sistem akan memberikan rekomendasi berdasarkan rekam jejak aktifitas pengguna. Maka itu, pada penelitian ini diusulkan penerapan metode content-boosted collaborative filtering (CBCF) untuk meningkatkan kemampuan sistem rekomendasi dalam pemberian rekomendasi untuk acara pernikahan, khususnya pada pengguna baru. Hasil yang diperoleh selanjutnya dibandingkan dengan dua metode yang umum digunakan yaitu content based recommendation (CB) dan collaborative filtering (CF). Berdasarkan hasil penelitian yang diperoleh, dapat disimpulkan bahwa CBCF dapat mempertahankan kualitas pemberian rekomendasi yang baik untuk pengguna lama dengan akurasi sebesar 84% serta mampu memberikan rekomendasi untuk pengguna baru dengan akurasi 54% yang mana kondisi ini tidak bisa diselesaikan oleh metode CB ataupun CF.Kata Kunci: bisnis digital, penyedia jasa acara pernikahan, sistem rekomendasi, content-boosted collaborative filtering

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Sukmana, Husni Teja, Siti Atinah, and Luh Kesuma Wardhani. "IMPLEMENTASI CONTENT-BASED FILTERING PADA APLIKASI RADAR ZAKAT DALAM MEREKOMENDASIKAN PREFERENSI MUSTAHIK." JURNAL TEKNIK INFORMATIKA 12, no. 2 (November 27, 2019): 167–76. http://dx.doi.org/10.15408/jti.v12i2.13172.

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Zakat is one of the pillars of Islam which is always mentioned parallel to prayer. The problems that exist in zakat institutions in Indonesia are low level of trust in muzaki in zakat payments through official institutions and tend to distribute zakat directly to mustahik. Zakat can attract sufficient attention from Muslim intellectuals, especially in the fields of research related to the development of zakat management. However, the growing zakat information system does not make it easier for muzaki to choose mustahik preferences, even though choice recommendations of mustahik is needed to make it easier for muzaki to choose mustahik preferences. The researcher was interested in applying the concept of recommendation in the Zakat Radar application by using the content based filtering method to produce a mustahik recommendation system with the term frequency inverse document frquency (tf-idf) technique.. This system is built using the Java programming language and MySQL as a database. The mustahik recommendation system has been successfully implemented in the Radar Zakat application, which produces 5 mustahik recommendations based on the highest weighting of the similarity of mustahik criteria chosen by the user. Similarity of mustahik criteria is based on the query of mustahik criteria chosen by the user, 5 queries of mustahik criteria are mustahik income, residence, facilities, number of dependents, and mustahik employment status.

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Kim, Sangyeon, Insil Huh, and Sangwon Lee. "No Movie to Watch: A Design Strategy for Enhancing Content Diversity through Social Recommendation in the Subscription-Video-On-Demand Service." Applied Sciences 13, no. 1 (December 26, 2022): 279. http://dx.doi.org/10.3390/app13010279.

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Increasing diversity is becoming crucial in recommender systems to address the “filter bubble” issue caused by accuracy-based algorithms. Diversity-oriented algorithms have been developed to solve this problem. However, this diversification has made it difficult for users to discover what they really want from the variety of information provided by the algorithm. Users spend their time wandering around the recommended content space but fail to find content they want to watch. Therefore, they rely on external services to gather information that does not appear on the recommended list. This could lead to a reduction in the services’ ability to compete with other subscription video on-demand (SVOD) services. To address this problem, this study proposes a human-centered approach to diversification through social recommendations. We conducted an experiment to understand how perceived diversity affects user perceptions and attitudes. Specifically, by incorporating social recommendations into the SVOD service, this experiment was changed to examine the following conditions: (1) influencers vs. online friends, and (2) human recommendation lists vs. algorithmic recommendation lists. The findings indicated that perceived diversity influences the manner in which the users perceive information quality and playfulness, both of which have a positive effect on their intention to use. Additionally, the participants’ perceptions of information quality were greater in the scenario with the human recommendation than in that with the algorithmic recommendation. This study contributes to the development of a theoretical framework based on perceived diversity through social recommendations and the design of an SVOD interface with social recommendations to provide better user experiences.

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Yoo, Youngtae, and Minjung Kang. "No Response Instead Of Stock Recommendations: Evidence From Korea." Journal of Applied Business Research (JABR) 31, no. 4 (July 10, 2015): 1563. http://dx.doi.org/10.19030/jabr.v31i4.9337.

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The objectives of this study are to confirm the theories and findings of empirical research related to stock recommendations in analysts reports and to examine from various angles the significance of stock recommendation revisions in the Korean capital market. In a considerable number of analysts reports, no stock recommendations are made; this area of the report remains blank. In this study, the blank is labeled No Response. We analyze the factors related to the decision to omit stock recommendations and the informational content of analysts reports in which no stock recommendations are made. There is no previous research on this phenomenon in Korea or other countries. Under the assumptions that the optimum portfolio of investors is affected by the trading of stocks, and that analysts reports reflect market expectations, we investigate the informational content of reports in which no stock recommendations are made by observing the abnormal returns on the day of disclosure.

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Malyeyeva, Olga, Vadym Yesipov, Roman Artiukh, and Viktor Kosenko. "IMPLEMENTATION OF A HYBRID METHOD OF SEARCHING FOR CLOSE OBJECTS, TAKING INTO ACCOUNT THE GENERAL AND ACOUSTIC CHARACTERISTICS." Innovative Technologies and Scientific Solutions for Industries, no. 1 (15) (March 31, 2021): 59–68. http://dx.doi.org/10.30837/itssi.2021.15.059.

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The subject of research in the article is the methods of finding close objects and technologies of forming recommendations. The aim of the article is to develop a recommendation system based on a hybrid method of searching for objects, taking into account both user preferences and audio characteristics of objects. The following tasks are solved: analysis of methods and algorithms used in recommendation systems; development of a hybrid method of forming recommendations on the principle of double organization; determination of the main functions and architecture of the system of formation of musical recommendations; testing of calculation algorithms and search methods in the system for analysis of similarity of musical recommendations. The following research methods are used: methods of correlation analysis, methods of similarity theory, algorithms of collaborative filtering and content analysis, hybrid methods, methods of analysis of audio characteristics, programming technologies. The following results were obtained: A study of collaborative filtering, content-based filtering and hybrid methods. Algorithms and calculation formulas of the considered methods are given. The main audio characteristics of musical compositions are considered. The method of formation of recommendations on the principle of double organization is developed. The main functions of the system of formation of musical recommendations are listed and the diagram of components is formed. An example of calculating the characteristics of user preferences and similarity of musical compositions by audio characteristics is given. Conclusions: According to the results of testing the system by three methods, we can conclude that the proposed hybrid method was the most effective among the studied recommendation methods with the lowest standard error rate. In addition, the hybrid method on the principle of double organization solves such problems of existing recommendation methods as excessive similarity of recommendations, potentially small number or no proposals at all by compensating data from one block of data from another.

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Farooq, Omar. "Information Content Of Analyst Recommendations: Evidence From The Danish Biotechnology Sector." Journal of Applied Business Research (JABR) 32, no. 2 (March 1, 2016): 379. http://dx.doi.org/10.19030/jabr.v32i2.9583.

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The purpose of this paper is to document the performance of analyst recommendations for biotechnology firms listed at the Copenhagen Stock Exchange during the period between 2001 and 2010. Our results show that analysts are able to reveal value relevant information via their recommendations. We report that buy recommendations are followed by significantly positive returns and sell recommendations are followed by significantly negative returns. However, we also show that performance of analyst recommendations is not uniform across all firms. It depends on the extent of information asymmetries present within firms. We show that analyst recommendations contain no value for firms with the least level of transparency (lowest intellectual capital disclosure, lowest analyst coverage, and lowest frequency of recommendations). However, as information environment improves, value of analyst recommendations also goes up. We recommend biotechnology firms to improve on their disclosure levels.

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Zhang, Yutong. "Sentiment Analysis and Personalized Recommendations Based on JD.com Reviews." Frontiers in Business, Economics and Management 4, no. 3 (July 31, 2022): 25–28. http://dx.doi.org/10.54097/fbem.v4i3.1048.

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The general big data personalized recommendation is based on the number of times or the length of time users click on a related content, but in many cases, these cannot be the most direct basis for accurate recommendation, and there may be cases such as wrong clicks by users. These factors and a large number of related products or articles recommended to users may cause users' disgust. This article conducts sentiment analysis on JD.com reviews as an example, obtains the user's likes and dislikes, and then makes accurate personalized recommendations, so that a greater understanding of preferences can improve the effect of recommendations, and more accurate personalized recommendations.

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Singh, Vivek Kumar, Shruthi E. Karnam, and Bhagyashri R. Hanji. "Orchestration of ML-Based Recommendation Systems." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 6, 2021): 173–80. http://dx.doi.org/10.51201/jusst/21/08340.

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Many e-commerce websites use recommendation systems to recommend products to users to boost sales and user experience. These recommendations do not always come from the same recommendation engine. Websites can use multiple recommender models that use different machine learning algorithms and neural networks to compute these recommendations. There arises a need for a machine learning pipeline that will help orchestrate all the steps required to compute and display recommendations. The pipeline handles training a model using content-based approach, storing it with required metadata, loading it, precomputing recommendations, collecting user metrics, analysing the metrics and retraining the models with updated hyperparameters if required. Without a pipeline to automate and streamline the process, much of the work must be done manually.

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Journal articles on the topic 'Content recommendations'

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