10 citations · 11 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…