18 citations · 53 across the 19 of their papers we have counts for
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…
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning
Zihan Liao, Jun Wang, Hang Yu +3
Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addr…
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…
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…
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…