Exploring lottery ticket hypothesis in media recommender systems

International Journal of Intelligent SystemsVolume 37, Issue 5 p. 3006-3024 RESEARCH ARTICLE Exploring lottery ticket hypothesis in media recommender systems Yanfang Wang, Yanfang Wang School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSearch for more papers by this authorYongduo Sui, Yongduo Sui School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSearch for more papers by this authorXiang Wang, Xiang Wang School of Computing, National University of Singapore, Kent Ridge, SingaporeSearch for more papers by this authorZhenguang Liu, Zhenguang Liu College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, ChinaSearch for more papers by this authorXiangnan He, Corresponding Author Xiangnan He [email protected] School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China Correspondence Xiangnan He, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230022, China. Email: [email protected]Search for more papers by this author Yanfang Wang, Yanfang Wang School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSearch for more papers by this authorYongduo Sui, Yongduo Sui School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSearch for more papers by this authorXiang Wang, Xiang Wang School of Computing, National University of Singapore, Kent Ridge, SingaporeSearch for more papers by this authorZhenguang Liu, Zhenguang Liu College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, ChinaSearch for more papers by this authorXiangnan He, Corresponding Author Xiangnan He [email protected] School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China Correspondence Xiangnan He, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230022, China. Email: [email protected]Search for more papers by this author First published: 17 January 2022 https://doi.org/10.1002/int.22827Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Media recommender systems aim to capture users' preferences and provide precise personalized recommendation of media content. There are two critical components in the common paradigm of modern recommender models: (1) representation learning, which generates an embedding for each user and item; and (2) interaction modeling, which fits user preferences toward items based on their representations. In spite of great success, when a great amount of users and items exist, it usually needs to create, store, and optimize a huge embedding table, where the scale of model parameters easily reach millions or even larger. Hence, it naturally raises questions about the heavy recommender models: Do we really need such large-scale parameters? We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model. In this paper, we extend LTH to media recommender systems, aiming to find the winning tickets in deep recommender models. To the best of our knowledge, this is the first work to study LTH in media recommender systems. With Matrix Factorization and Light Graph Convolution Networks as the backbone models, we found that there widely exist winning tickets in recommender models. On three media convergence data sets—Yelp2018, TikTok and Kwai, the winning tickets can achieve comparable recommendation performance with only 29 % ~ 48 % , 7 % ~ 10 %, and 3 % ~ 17 % of parameters, respectively. Volume37, Issue5Special Issue: Intelligent Information Processing and Services in Media ConvergenceMay 2022Pages 3006-3024 RelatedInformation