1.5k citations · 2.3k across the 39 of their papers we have counts for
7 papers · 1 filter
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…
Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
Chen Yang, Sunhao Dai, Yupeng Hou +4
Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However…
Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts
Tingchen Fu, Yupeng Hou, Julian McAuley +1
The task of multi-objective alignment aims at balancing and controlling the different alignment objectives (e.g., helpfulness, harmlessness and honesty) of large language models to…
Multi-Behavior Generative Recommendation
Zihan Liu, Yupeng Hou, Julian McAuley
Multi-behavior sequential recommendation (MBSR) aims to incorporate behavior types of interactions for better recommendations. Existing approaches focus on the next-item prediction…
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Junda Wu, Cheng-Chun Chang, Tong Yu +4
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language mod…
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…
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
Jianing Wang, Junda Wu, Yupeng Hou +3
Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers…