Semantic retrieval in e-commerce search aims to identify a compact candidate set from billion-scale product catalogs with both high recall and low latency. Dual-Encoders dominate this stage due to their efficient dot-product similarity, but this formulation limits model expressiveness and fails to capture fine-grained...
Guo-Hao Tan, Jia-Hui Wan, Tao Wen et al.· Proceedings of the 20th ACM...· 0 citations
Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale item spaces. However, most existing methods follow a history-as-context paradigm that repeatedly reconstructs user preference from behavior h...
Hui Qian, Chang-Fa Wu, Chang Liu et al.· arXiv.org· 0 citations
This work proposes the Cluster-Ranked Identifier (CRID), which decouples DocID into semantic clustering and business-value ranking, yielding collision-free identifiers that support incremental updates via intra-cluster reranking.
Gui Ling, Zhihong Chen, Yu Li et al.· arXiv.org· 2 citations
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains...
Dan Ou, Gui Ling, Haokai Wan et al.· 0 citations
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