4 papers
LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
Qin Ren, Zheng Chai, Xijun Xiao +2
Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the rankin…
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
Jie Zhu, Zhifang Fan, Xiaoxie Zhu +18
Recent progress on large language models (LLMs) has spurred interest in scaling up recommendation systems, yet two practical obstacles remain. First, training and serving cost on i…
LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
Zheng Chai, Qin Ren, Xijun Xiao +14
Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems. Existing solutions typically rely on…
Large Memory Network for Recommendation
Hui Lu, Zheng Chai, Yuchao Zheng +5
Modeling user behavior sequences in recommender systems is essential for understanding user preferences over time, enabling personalized and accurate recommendations for improving…