3 papers
cs.IR2026
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Qingyun Liu, Bo Yan, Yang Liu +15
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emergi…
cs.IR2025
A Plug-and-play Model-agnostic Embedding Enhancement Approach for Explainable Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao +2
Existing multimedia recommender systems provide users with suggestions of media by evaluating the similarities, such as games and movies. To enhance the semantics and explainabilit…
cs.IR2025
Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao +2
Personalized federated recommendation system (FedRec) has gained significant attention for its ability to preserve privacy in delivering tailored recommendations. To alleviate the…