3 papers
cs.IR2020
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Wayne Xin Zhao, Junhua Chen, Pengfei Wang +2
Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriat…
cs.IR2017
Preference Modeling by Exploiting Latent Components of Ratings
Junhua Chen, Wei Zeng, Junming Shao +1
Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given u…
cs.SI2017
A Price Driven Hazard Approach to User Retention
Junhua Chen, Wei Zeng, Ge Fan +1
Customer loyalty is crucial for internet services since retaining users of a service to ensure the staying time of the service is of significance for increasing revenue. It demands…