6 citations · 14 across the 5 of their papers we have counts for
5 papers
Sequential Recommendation with Latent Relations based on Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…
Sequence-level Semantic Representation Fusion for Recommender Systems
Lanling Xu, Zhen Tian, Bingqian Li +4
With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the u…
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Chenrui Zhang, Lin Liu, Jinpeng Wang +4
As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To furt…
Enhancing Personalized Ranking With Differentiable Group AUC Optimization
Xiao Sun, Bo Zhang, Chenrui Zhang +2
AUC is a common metric for evaluating the performance of a classifier. However, most classifiers are trained with cross entropy, and it does not optimize the AUC metric directly, w…