74 citations · 200 across the 11 of their papers we have counts for
5 papers · 1 filter
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
Yanan Wang, Yong Ge, Li Li +2
Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large num…
Explainable Recommender Systems via Resolving Learning Representations
Ninghao Liu, Yong Ge, Li Li +3
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing mor…
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
Xi Liu, Li Li, Ping-Chun Hsieh +3
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experie…
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
Xi Liu, Muhe Xie, Xidao Wen +4
As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have b…
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
Xi Liu, Muhe Xie, Xidao Wen +4
Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical cha…