Industrial recommender systems typically integrate multiple objectives—such as clicks, watch time, likes, and follows—to perform a holistic ranking. However, effectively fusing these diverse tasks to reflect overall user satisfaction remains a formidable challenge. Existing approaches struggle with distributional discr...
Qing Luo, Ge Chen, Huayi Shen et al.· Proceedings of the 32nd ACM...· 0 citations
The proposed OneModel, a unified framework for multi-stream final ranking, maps heterogeneous behaviors into shared event sequences, learns long-context user representations with an action-oriented backbone, and introduces scenario-aware Information Modulation to balance cross-stream transfer and stream-specific specia...
Yinqi Zhang, Pei-Yu Hu, Yuntian Tang et al.· 1 citation
RedGR is proposed, a generative retrieval model that unifies the modeling of multiple complex retrieval tasks and has been fully depolyed in the homefeed recommendation scenario of RedNote, serving hundreds of millions of users every day.
Mengcheng Fang, Hongyu Wang, Xichuan Niu et al.· Annual International ACM SIG...· 0 citations
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