Generative recommendation (GR) represents user actions as sequences of discrete tokens (often referred to as semantic IDs) and predict future interactions through autoregressive generation. This paradigm offers advantages such as compact representations, scalability, and the potential to unify retrieval and ranking. Ho...
Qi-Yong Zhong, Jia-Jie Su, Yun-Shan Ma et al.· Proceedings of the 35th ACM...· 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 workshop aims to bring together researchers and practitioners to discuss recent advances, practical applications, and future directions of AI agents in information retrieval, while encouraging collaboration and knowledge exchange within the community.
Qing-Song Wen, P. Mehrotra, Yongfeng Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
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