192 citations · 342 across the 11 of their papers we have counts for
11 papers
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
Jinze Wang, Lu Zhang, Zhu Sun +1
Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the cit…
A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation
Huizi Wu, Hui Fang, Zhu Sun +3
Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve reco…
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Zhu Sun, Yu Lei, Lu Zhang +3
Current study on next POI recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the se…
Understanding Diversity in Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Mean…
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Zhu Sun, Hui Fang, Jie Yang +5
Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproduci…
Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation
Zhuoyi Lin, Sheng Zang, Rundong Wang +4
Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality.…
Adversary Agnostic Robust Deep Reinforcement Learning
Xinghua Qu, Yew-Soon Ong, Abhishek Gupta +1
Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear…
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Xinghua Qu, Zhu Sun, Yew-Soon Ong +2
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing ever…
Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation
Xiao Sha, Zhu Sun, Jie Zhang
Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and complementary information to u…
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
Zhu Sun, Qing Guo, Jie Yang +4
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…
Interacting Attention-gated Recurrent Networks for Recommendation
Wenjie Pei, Jie Yang, Zhu Sun +3
Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history…