Exploring lottery ticket hypothesis in media recommender systems. Issue 5 (17th January 2022)

• Record Type: Journal Article
• Authors: Wang, Yanfang; Sui, Yongduo; Wang, Xiang; Liu, Zhenguang; He, Xiangnan
• Other Names: Wang Meng guestEditor.; Zhang Chi guestEditor.; Hao Shijie guestEditor.; Yu Jun guestEditor.; Mu Tingting guestEditor.
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 5(2022)
• Page Start: 3006
• Page End: 3024
• Publication Date: 2022-01-17
• Subjects: iterative magnitude‐based pruning -- lightweight embedding -- lottery ticket hypothesis -- media recommender system -- model pruning; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22827
• Languages: English
• ISSNs: 0884-8173

Abstract

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.