9 papers
LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation
Yu Xia, Zihan Lin, Wei Yang +4
Large Language Models (LLMs) have shown great promise in recommendation due to superior reasoning abilities. However, existing methods mainly rely on explicit Chain-of-Thought (CoT…
TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Wei Yang, Rui Zhong, Zihan Lin +4
Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exh…
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…
Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
Jingtong Gao, Ling Pan, Yejing Wang +6
Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optim…
TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
Yu Xia, Rui Zhong, Zeyu Song +5
The extensive world knowledge and powerful reasoning capabilities of large language models (LLMs) have attracted significant attention in recommendation systems (RS). Specifically,…