3 papers
cs.IR2025
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion
Moyu Zhang, Yongxiang Tang, Yujun Jin +2
Traditional recommendation methods rely on correlating the embedding vectors of item IDs to capture implicit collaborative filtering signals to model the user's interest in the tar…
cs.IR2024
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
Yongxiang Tang, Wentao Bai, Guilin Li +2
In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall…
cs.IR2024
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
Moyu Zhang, Yongxiang Tang, Jinxin Hu +1
Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampe…