1 citations · 3 across the 19 of their papers we have counts for
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FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning
Wei Yang, Rui Zhong, Yiqun Chen +4
Multimodal recommendation aims to enhance user preference modeling by leveraging rich item content such as images and text. Yet dominant systems fuse modalities in the spatial doma…
Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
Wei Yang, Rui Zhong, Yiqun Chen +2
Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noi…
Leveraging LLMs to Evaluate Usefulness of Document
Xingzhu Wang, Erhan Zhang, Yiqun Chen +7
The conventional Cranfield paradigm struggles to effectively capture user satisfaction due to its weak correlation between relevance and satisfaction, alongside the high costs of r…
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Qi Liu, Haozhe Duan, Yiqun Chen +3
Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the perfo…
TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
Yiqun Chen, Qi Liu, Yi Zhang +6
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs f…
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
Yiqun Chen, Jiaxin Mao, Yi Zhang +7
Search result diversification (SRD), which aims to ensure that documents in a ranking list cover a broad range of subtopics, is a significant and widely studied problem in Informat…