{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ranking-social-media-news-feeds-a-comparative","title":"Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models","arxiv_id":null,"date":"2022-03-12","proceeding":"International Conference on Artificial Intelligence and its Applications 2022 3","authors":["Sami Belkacem","Kamel Boukhalfa","Omar Boussaid"],"abstract":"Home  Artificial Intelligence and Its Applications  Conference paper\r\nRanking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models\r\nSami Belkacem, Kamel Boukhalfa & Omar Boussaid \r\nConference paper\r\nFirst Online: 12 March 2022\r\n416 Accesses\r\n\r\nPart of the Lecture Notes in Networks and Systems book series (LNNS,volume 413)\r\n\r\nAbstract\r\nRanking news feed updates by relevance has been proposed to help social media users catch up with the content they may find interesting. For this matter, a single non-personalized model has been used to predict the relevance for all users. However, as user interests and preferences are different, we believe that using a personalized model for each user is crucial to refine the ranking. In this work, to predict the relevance of news feed updates and improve user experience, we use the random forest algorithm to train and introduce a personalized prediction model for each user. Then, we compare personalized and non-personalized models according to six criteria: (1) the overall prediction performance; (2) the amount of data in the training set; (3) the cold-start problem; (4) the incorporation of user preferences over time; (5) the model fine-tuning; and (6) the personalization of feature importance for users. Experimental results on Twitter show that a single non-personalized model for all users is easy to manage and fine-tune, is less likely to overfit, and it addresses the problem of cold-start and inactive users. On the other hand, the personalized models we introduce allow personalized feature importance, take into consideration the preferences of each user, and allow to track changes in user preferences over time. Furthermore, personalized models give a higher prediction accuracy than non-personalized models.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-96311-8_19","url_pdf":"http://dspace.univ-eloued.dz/bitstream/123456789/10831/1/ranking%20social%20media%20news%20feeds%20a%20comparative%20study%20of%20personalized%20and%20non%20personalized.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ranking-social-media-news-feeds-a-comparative","repo_url":"https://github.com/SamBelkacem/Ranking-social-media-news-feed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"news-recommendation","task_name":"News Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"social-media-popularity-prediction","task_name":"Social Media Popularity Prediction"}],"methods":[],"datasets_introduced":[{"slug":"ranking-social-media-news-feed","name":"Ranking social media news feed","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}