6 papers
Harnessing Image Question Dependence for Better VLM Test-time Reinforcement Learning
Xinrui He, Ting-Wei Li, Junting Wang +4
Test-time reinforcement learning can adapt vision-language models (VLMs) to unlabeled target data, but its effectiveness is fundamentally limited by the reliability of self-generat…
Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation
Ruizhong Qiu, Yinglong Xia, Dongqi Fu +6
Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical beha…
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
Zhining Liu, Ziyi Chen, Hui Liu +9
Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present…
Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
Hyunsik Yoo, Ting-Wei Li, SeongKu Kang +4
While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time.…
Continual Recommender Systems
Hyunsik Yoo, SeongKu Kang, Hanghang Tong
Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without fo…