From the 2 of 19 linked papers with an AI index.
11 papers · 1 filter
Role of Personality in Conversational Information Seeking
Abdisalam Abukar, Junchen Fu, Chengli Zhai +1
Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does m…
RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation
Wenhao Deng, Junchen Fu, Hanwen Du +6
The paper introduces RecRec, a framework that separates reasoning from prediction in sequential recommendation by compressing user histories into multiple latent interests and recu…
Stream-aware Side Adaptation for Large Pre-trained Multimodal Embedding Models in Sequential Recommendation
Junchen Fu, Kaiwen Zheng, Ioannis Arapakis +4
Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item re…
The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +6
Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA,…
Differentiable Semantic ID for Generative Recommendation
Junchen Fu, Xuri Ge, Alexandros Karatzoglou +4
Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as…
Are Multimodal Embeddings Truly Beneficial for Recommendation? A Deep Dive into Whole vs. Individual Modalities
Yu Ye, Junchen Fu, Yu Song +2
Multimodal recommendation has emerged as a mainstream paradigm, typically leveraging text and visual embeddings extracted from pre-trained models such as Sentence-BERT, Vision Tran…