Speaker: Dr.Chunfeng Yang, Research Institute of Tencent

Time and Date: 15:00-16:00pm, October 14, 2017

Place: Room 521, Physics Building, Handan Campus

Abstract

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 site-specific 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.

Biography

杨春风，2012年于华中科技大学电子信息工程系获得学士学位，2017年于香港中文大学信息工程系获得博士学位。博士研究方向：推荐系统，（大数据）数据挖掘，社交网络分析，计算广告，用户建模。2014~2017在腾讯数据平台部实习，从事社交化视频推荐和计算广告的研究和实践。现在为腾讯公司数据平台部高级研究员，从事社交广告相关的研发。