1.5k citations · 2.3k across the 39 of their papers we have counts for
9 papers · 1 filter
OpticalRec: Unified Optical Vision-Language Representation for Multimodal Recommendation
Yueqi Wang, Zitian Guo, Yupeng Hou +9
Recent advances in vision-language modeling have substantially improved multimodal encoding, retrieval and reasoning. Yet for multimodal recommendation, encoding rich item vision-l…
Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
Xi Chu, Yupeng Hou
Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel. We study brand dynamics i…
BOOKMARKS: Efficient Active Storyline Memory for Role-playing
Letian Peng, Ziche Liu, Yiming Huang +4
Memory systems are critical for role-playing agents (RPAs) to maintain long-horizon consistency. However, existing RPA memory methods (e.g., profiling) mainly rely on incremental s…
MLPs are Efficient Distilled Generative Recommenders
Zitian Guo, Yupeng Hou, Clark Mingxuan Ju +2
Generative recommendation models employing Semantic IDs (SIDs) exhibit strong potential, yet their practical deployment is bottlenecked by the high inference latency of beam-expand…
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…
From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
Pengyue Jia, Xiaobei Wang, Yingyi Zhang +14
In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user sati…
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…
Give Users the Wheel: Towards Promptable Recommendation Paradigm
Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang +6
Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these architectures remain structurally blind to exp…
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…