3 citations · 6 across the 16 of their papers we have counts for
7 papers · 1 filter
Markovian Pre-Trained Transformer for Next-Item Recommendation
Cong Xu, Guoliang Li, Jun Wang +1
We introduce the Markovian Pre-trained Transformer (MPT) for next-item recommendation, a transferable model fully pre-trained on synthetic Markov chains, yet capable of achieving s…
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information
Yunhang He, Cong Xu, Jun Wang +1
Graph Neural Networks (GNNs) have demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data fo…
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…
Explainable Session-based Recommendation via Path Reasoning
Yang Cao, Shuo Shang, Jun Wang +1
This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditiona…
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…
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Cong Xu, Jun Wang, Jianyong Wang +1
Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models. I…