5 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Generative Archetype-Grounded Item Representations for Sequential Recommendation
Yifan Li, Jiahong Liu, Xinni Zhang +5
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a…
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering
Yankai Chen, Yue Que, Xinni Zhang +2
Learning vectorized embeddings is fundamental to many recommender systems for user-item matching. To enable efficient online inference, representation binarization, which embeds la…
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Yuhan Li, Xinni Zhang, Linhao Luo +4
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness,…
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Xinni Zhang, Yankai Chen, Cuiyun Gao +3
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention. Despite the success in general recommendation scenarios, p…