6 citations · 19 across the 15 of their papers we have counts for
9 papers · 1 filter
Fine-grained Semantics Integration for Large Language Model-based Recommendation
Jiawei Feng, Xiaoyu Kong, Leheng Sheng +8
Recent advances in Large Language Models (LLMs) have driven a shift in recommender systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommen…
MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation
Xiaoyu Kong, Leheng Sheng, Junfei Tan +5
The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by…
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Yu Shang, Peijie Liu, Yuwei Yan +9
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasonin…
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Xiaoyu Kong, Jiancan Wu, An Zhang +4
Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences. Leveraging the strength…
Language Representations Can be What Recommenders Need: Findings and Potentials
Leheng Sheng, An Zhang, Yi Zhang +3
Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields. However, i…
On Softmax Direct Preference Optimization for Recommendation
Yuxin Chen, Junfei Tan, An Zhang +5
Recommender systems aim to predict personalized rankings based on user preference data. With the rise of Language Models (LMs), LM-based recommenders have been widely explored due…
General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
An Zhang, Wenchang Ma, Pengbo Wei +2
Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood inform…
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
An Zhang, Leheng Sheng, Zhibo Cai +2
Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into…
On Generative Agents in Recommendation
An Zhang, Yuxin Chen, Leheng Sheng +2
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Add…