15 citations · 38 across the 17 of their papers we have counts for
12 papers · 1 filter
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…
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…
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Jiayi Liao, Ruobing Xie, Sihang Li +4
Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited con…
Position-aware Graph Transformer for Recommendation
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have adva…
RosePO: Aligning LLM-based Recommenders with Human Values
Jiayi Liao, Xiangnan He, Ruobing Xie +5
Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scena…
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…