9 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…
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…
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…
Reinforced Prompt Personalization for Recommendation with Large Language Models
Wenyu Mao, Jiancan Wu, Weijian Chen +3
Designing effective prompts can empower LLMs to understand user preferences and provide recommendations with intent comprehension and knowledge utilization capabilities. Neverthele…
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…
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…