29 papers
Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation
Haiying He, Xiaopeng Li, Yuchen Gu +9
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user beh…
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
Ziwei Liu, Yejing Wang, Wanyu Wang +6
Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical us…
RAGR: Review-Augmented Generative Recommendation
Yingyi Zhang, Junyi Li, Yejing Wang +8
Sequential recommendation (SR) is traditionally formulated as next-item prediction over chronological item interactions. Although recent generative recommendation (GR) methods intr…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
Ziwei Liu, Qidong Liu, Wanyu Wang +6
Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains. Despite current progress, the im…
Conditional Memory Enhanced Item Representation for Generative Recommendation
Ziwei Liu, Yejing Wang, Shengyu Zhou +2
Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follo…