11 papers
Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation
Yupeng Hou, Haven Kim, Clark Mingxuan Ju +3
Generative recommendation (GR) models generate items by autoregressively producing a sequence of discrete tokens that jointly index the target item. However, this autoregressive ge…
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…
How Well Does Generative Recommendation Generalize?
Yijie Ding, Zitian Guo, Jiacheng Li +8
A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic…
AgenticTagger: Structured Item Representation for Recommendation with LLM Agents
Zhouhang Xie, Bo Peng, Zhankui He +11
High-quality representations are a core requirement for effective recommendation. In this work, we study the problem of LLM-based descriptor generation, i.e., keyphrase-like natura…
FusID: Modality-Fused Semantic IDs for Generative Music Recommendation
Haven Kim, Yupeng Hou, Julian McAuley
Generative recommendation systems have achieved significant advances by leveraging semantic IDs to represent items. However, existing approaches that tokenize each modality indepen…
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…