74 citations · 200 across the 11 of their papers we have counts for
11 papers
Boosting Factorization Machines via Saliency-Guided Mixup
Chenwang Wu, Defu Lian, Yong Ge +3
Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive fea…
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…
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Le Wu, Junwei Li, Peijie Sun +3
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborati…
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…
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
Haoyu Wang, Defu Lian, Yong Ge
The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative sample…
Personalized Multimedia Item and Key Frame Recommendation
Le Wu, Lei Chen, Yonghui Yang +4
When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e.g., the poster of a movie). As each multimedia item…
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…
A Hierarchical Attention Model for Social Contextual Image Recommendation
Le Wu, Lei Chen, Richang Hong +3
Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences on use…
A Context-aware Attention Network for Interactive Question Answering
Huayu Li, Martin Renqiang Min, Yong Ge +1
Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from s…