15 citations · 36 across the 12 of their papers we have counts for
13 papers
On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
Wenyu Mao, Jiancan Wu, Guoqing Hu +3
Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories…
Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning
Yutian Liu, Zhengyi Yang, Jiancan Wu +1
Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personali…
Addressing Missing Data Issue for Diffusion-based Recommendation
Wenyu Mao, Zhengyi Yang, Jiancan Wu +4
Diffusion models have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences. However, the…
AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
Junkang Wu, Xue Wang, Zhengyi Yang +5
Aligning large language models (LLMs) with human values and intentions is crucial for their utility, honesty, and safety. Reinforcement learning from human feedback (RLHF) is a pop…
-DPO: Direct Preference Optimization with Dynamic
Junkang Wu, Yuexiang Xie, Zhengyi Yang +5
Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO…
Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization
Junkang Wu, Yuexiang Xie, Zhengyi Yang +6
This study addresses the challenge of noise in training datasets for Direct Preference Optimization (DPO), a method for aligning Large Language Models (LLMs) with human preferences…
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…
Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang +4
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Lang…
MolTC: Towards Molecular Relational Modeling In Language Models
Junfeng Fang, Shuai Zhang, Chang Wu +6
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of la…
LLaRA: Large Language-Recommendation Assistant
Jiayi Liao, Sihang Li, Zhengyi Yang +4
Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has spark…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang +3
Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studie…
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Chengpeng Li, Zhengyi Yang, Jizhi Zhang +4
Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, reco…