Modern recommender systems advance not only by scaling data and parameters, but also by encoding task-specific inductive biases through architecture, including sparse feature interactions for click-through rate (CTR) prediction, temporal attention for sequential recommendation, and expert routing for multi-task learnin...
Xiao-Peng Li, Kuo Cai, Bo Chen et al.· 0 citations
Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training larger dense models from scratch demands substantial data and time, while their growing computation conflicts with the strict serving budgets of industrial sy...
Rui-Hao Zhang, Bo Chen, Xiao Wang et al.· 0 citations
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate items. Existing methods have evolved along two directions. One line dynamically retrieves target-relevant behaviors from long histories, en...
Rui Zhou, Bo Chen, Qinglin Jia et al.· 0 citations
This work comprehensively surveys recent generative recommendation advances through a tri-decoupled perspective, summarize the evolution of tokenization strategies, analyze the trade-offs of major generative architectures, and summarize the transition from supervised next-token prediction to reinforcement-learning-base...
Xiao-Peng Li, Yejing Wang, Hong-Hui Bao et al.· Proceedings of the 32nd ACM...· 0 citations
In the current digital ecosystem, recommender systems serve as the core infrastructure for navigating large-scale content catalogs and delivering personalized services, typically following multi-stage discriminative pipelines (e.g., retrieval, ranking, and re-ranking). However, their fragmented architectures cause casc...
Xiaopeng Li, Yejing Wang, Honghui Bao et al.· Proceedings of the 32nd ACM...· 0 citations
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