16 citations · 46 across the 36 of their papers we have counts for
6 papers · 2 filters
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
Ziwei Liu, Yejing Wang, Wanyu Wang +6
Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical us…
LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
Ziwei Liu, Qidong Liu, Wanyu Wang +6
Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains. Despite current progress, the im…
M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation
Zhiyou Xiao, Qinhan Yu, Binghui Li +3
Current research on Multimodal Retrieval-Augmented Generation (MRAG) enables diverse multimodal inputs but remains limited to single-modality outputs, restricting expressive capaci…
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
Qidong Liu, Xiangyu Zhao, Yejing Wang +6
Cross-domain Sequential Recommendation (CDSR) aims to extract the preference from the user's historical interactions across various domains. Despite some progress in CDSR, two prob…
A Survey of Multimodal Retrieval-Augmented Generation
Lang Mei, Siyu Mo, Zhihan Yang +1
Multimodal Retrieval-Augmented Generation (MRAG) enhances large language models (LLMs) by integrating multimodal data (text, images, videos) into retrieval and generation processes…
CogPlanner: Unveiling the Potential of Agentic Multimodal Retrieval Augmented Generation with Planning
Xiaohan Yu, Zhihan Yang, Chong Chen
Multimodal Retrieval Augmented Generation (MRAG) systems have shown promise in enhancing the generation capabilities of multimodal large language models (MLLMs). However, existing…