5 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…
D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
Zihan Liao, Hang Yu, Jianguo Li +2
The key challenge in semantic search is to create models that are both accurate and efficient in pinpointing relevant sentences for queries. While BERT-style bi-encoders excel in e…
Estimating Difficulty Levels of Programming Problems with Pre-trained Model
Zhiyuan Wang, Wei Zhang, Jun Wang
As the demand for programming skills grows across industries and academia, students often turn to Programming Online Judge (POJ) platforms for coding practice and competition. The…
Data-free Knowledge Distillation for Fine-grained Visual Categorization
Renrong Shao, Wei Zhang, Jianhua Yin +1
Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the ex…