Multi-site User Behavior Modeling and Its Application in Video Recommendation
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摘要As online video service continues to grow in popularity, video content providers compete hard for more eyeball engagement. Some users visit multiple video sites to enjoy videos of their interest while some visit exclusively one site. However, due to the isolation of data, mining and exploiting user behaviors in multiple video websites
remain unexplored so far. In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China. The empirical study shows that users exhibit both consistent cross-site interests as well as sitespecific interests. To represent this dichotomous pattern of user preferences, we propose a generative model of Multi-site Probabilistic Factorization (MPF) to capture both the cross-site as well as
site-specific preferences. Besides, we discuss the design principle of our model by analyzing the sources of the observed site-specific user preferences, namely, site peculiarity and data sparsity. Through conducting extensive recommendation validation, we show that our MPF model achieves the best results compared to several other
state-of-the-art factorization models with significant improvements of F-measure by 12.96%, 8.24% and 6.88%, respectively. Our findings provide insights on the value of integrating user data from
multiple sites, which stimulates collaboration between video service providers.
著者Chunfeng Yang, Huan Yan, Donghan Yu, Yong Li, Dah Ming Chiu
會議名稱The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
會議地點Shinjuku, Tokyo

上次更新時間 2018-18-01 於 11:17