5 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…
HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control
Xincheng Yao, Ruoqi Li, Cheng Chen +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, the de f…
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…
HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation
Jingyi Zhou, Cheng Chen, Kai Zuo +5
Large language models (LLMs) have recently demonstrated strong potential for sequential recommendation. However, current LLM-based approaches face critical limitations in modeling…
Cross-Scenario Unified Modeling of User Interests at Billion Scale
Manjie Xu, Cheng Chen, Xin Jia +9
User interests on content platforms are inherently diverse, manifesting through complex behavioral patterns across heterogeneous scenarios such as search, feed browsing, and conten…