6 papers
CAPTS: Channel-Aware, Preference-Aligned Trigger Selection for Multi-Channel Item-to-Item Retrieval
Xiaoyou Zhou, Yuqi Liu, Zhao Liu +4
Large-scale industrial recommender systems commonly adopt multi-channel retrieval for candidate generation, combining direct user-to-item (U2I) retrieval with two-hop user-to-item-…
SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity
Yukun Zhang, Si Dong, Xu Wang +11
Industrial recommender systems typically rely on multi-task learning to estimate diverse user feedback signals and aggregate them for ranking. Recent advances in model scaling have…
OpenOneRec Technical Report
Guorui Zhou, Honghui Bao, Jiaming Huang +44
While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
Zhiyang Zhang, Junda She, Kuo Cai +8
Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerg…
LLMDiRec: LLM-Enhanced Intent Diffusion for Sequential Recommendation
Bo-Chian Chen, Manel Slokom
Existing sequential recommendation models, even advanced diffusion-based approaches, often struggle to capture the rich semantic intent underlying user behavior, especially for new…
CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics
Wanru Zhao, Hongxiang Fan, Shell Xu Hu +3
Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collabora…