7 papers · 1 filter
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Haoran Ling, Yuecheng Li, Zeyu Song +5
Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can…
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Yuecheng Li, Zeyu Song, Jing Yao +3
Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID spac…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems
Runpeng Cui, Zhipeng Sun, Chi Lu +1
Predicting continuous values such as watch-time and gross merchandise value (GMV) is a core problem in industrial recommendation systems. Its inherent difficulty stems from the hig…
RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment
Yuecheng Li, Hengwei Ju, Zeyu Song +4
Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic hetero…
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…