6 citations · 13 across the 5 of their papers we have counts for
5 papers
Deep Context Interest Network for Click-Through Rate Prediction
Xuyang Hou, Zhe Wang, Qi Liu +3
Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus…
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Sanshi Yu, Zhuoxuan Jiang, Dong-Dong Chen +4
Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with th…
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
Ruobing Xie, Qi Liu, Shukai Liu +4
Recently, real-world recommendation systems need to deal with millions of candidates. It is extremely challenging to conduct sophisticated end-to-end algorithms on the entire corpu…
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Wen Wang, Wei Zhang, Shukai Liu +4
Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based b…