5 citations · 15 across the 12 of their papers we have counts for
6 papers · 1 filter
Purely Semantic Indexing for LLM-based Generative Recommendation and Retrieval
Ruohan Zhang, Jiacheng Li, Julian McAuley +1
Semantic identifiers (IDs) have proven effective in adapting large language models for generative recommendation and retrieval. However, existing methods often suffer from semantic…
Inductive Generative Recommendation via Retrieval-based Speculation
Yijie Ding, Jiacheng Li, Julian McAuley +1
Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. While this…
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
Yupeng Hou, Jiacheng Li, Xiangjun Fu +4
Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging. Recent advances in large language models (LL…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
An Yan, Zhankui He, Jiacheng Li +2
Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new t…
Coarse-to-Fine Sparse Sequential Recommendation
Jiacheng Li, Tong Zhao, Jin Li +5
Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-t…