Modeling lifetime user behavior sequences is essential for capturing long-range preferences in industrial recommendation, yet 100K-length user sequences make target-aware ranking prohibitively expensive. Search-based methods that retrieve from raw sequences incur candidate-dependent costs that grow with sequence length...
Wen-Hao Li, Qing-Lin Jia, Cheng-Hou Jin et al.· 0 citations
The effectiveness of Inherit4Rec for continual capacity expansion and computation-efficient sparse conversion in industrial recommender systems is demonstrated, and both transformations consistently outperform the evaluated inheritance baselines across all prediction objectives.
Rui-Hao Zhang, Bo Chen, Xiao Wang et al.· 0 citations
SITA enables target-aware compression by organizing compressed interests into semantic structures through semantic identifiers learned via parallel semantic quantization, conditioned on the semantic identifier of the target item, and adaptively aggregates the corresponding structured interests to construct the target-s...
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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