Query-based recommendation separates expressing user needs from locating matching items. Its central learning problem is how to turn item interactions into effective retrieval requests when interaction logs do not annotate queries. We present R2QRec, which uses a fixed retrieval system to construct query supervision an...
Weixin Chen, Shu-Chang Liu, Hai-Lan Yang et al.· 0 citations
As a critical stage in the modern multi-stage recommendation pipeline, list-wise reranking determines the final exposed item sequence and directly shapes the user experience by modeling complex dependencies among candidate items within a list. Existing methods typically formulate this task as selecting indices from the...
Peng-Yue Jia, Xiao-Bei Wang, Yingyi Zhang et al.· Proceedings of the 35th ACM...· 0 citations
This work proposes GUIDER (Generative User Interest Discovery & Explicit Reasoning), a framework that fundamentally decouples intent planning from item matching by reformulating sequential modeling within a rigorous closed-set semantic interest space.
Jin-Ke Wu, Ying-Hao Wu, Shuchang Liu et al.· Proceedings of the 32nd ACM...· 0 citations
The emergence of Large Language Models (LLMs) has driven a paradigm shift in sequential recommendation from discriminative ranking to generative modeling. However, existing generative approaches predominantly rely on semantic IDs (SIDs)—discrete identifiers derived from hierarchical quantization that function as semant...
Jinke Wu, Yinghao Wu, Shuchang Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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