1 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
Cong Xu, Yunhang He, Jun Wang +1
While the mining of modalities is the focus of most multimodal recommendation methods, we believe that how to fully utilize both collaborative and multimodal information is pivotal…
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential Recommenders
Cong Xu, Zhangchi Zhu, Mo Yu +3
Large language models (LLMs) have been garnering increasing attention in the recommendation community. Some studies have observed that LLMs, when fine-tuned by the cross-entropy (C…
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation
Cong Xu, Zhangchi Zhu, Jun Wang +2
Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full soft…
Pattern-wise Transparent Sequential Recommendation
Kun Ma, Cong Xu, Zeyuan Chen +1
A transparent decision-making process is essential for developing reliable and trustworthy recommender systems. For sequential recommendation, it means that the model can identify…