1 citations · 1 across the 10 of their papers we have counts for
10 papers
ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
Yunhang He, Cong Xu, Zhangchi Zhu +2
Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show th…
Break the Optimization Barrier of LLM-Enhanced Recommenders: A Theoretical Analysis and Practical Framework
Zhangchi Zhu, Wei Zhang
Large language model (LLM)-enhanced recommendation models inject LLM representations into backbone recommenders to exploit rich item text without inference-time LLM cost. However,…
Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems
Zhangchi Zhu, Wei Zhang
This paper analyzes Cross-Entropy (CE) loss in knowledge distillation (KD) for recommender systems. KD for recommender systems targets at distilling rankings, especially among item…
Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
Minhao Wang, Yunhang He, Cong Xu +4
Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exh…
Exploring Feature-based Knowledge Distillation for Recommender System: A Frequency Perspective
Zhangchi Zhu, Wei Zhang
In this paper, we analyze the feature-based knowledge distillation for recommendation from the frequency perspective. By defining knowledge as different frequency components of the…
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…
Contrastive Learning with Negative Sampling Correction
Lu Wang, Chao Du, Pu Zhao +8
As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In…
Preference-Consistent Knowledge Distillation for Recommender System
Zhangchi Zhu, Wei Zhang
Feature-based knowledge distillation has been applied to compress modern recommendation models, usually with projectors that align student (small) recommendation models' dimensions…
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
Zhangchi Zhu, Lu Wang, Pu Zhao +7
Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU lea…