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cs.IR2026
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Rongcheng Lin, Yan Sun, Jamey Zhang +4
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models ov…
cs.IR2026
Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval
Ivan Ji, Liuyi Hu, Harrison +6
The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically inv…
cs.IR2026
Efficient Sequential Recommendation for Long Term User Interest Via Personalization
Qiang Zhang, Hanchao Yu, Ivan Ji +14
Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for seque…