7 papers
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code
Jian Xie, Zhendong Chu, Aoxiao Zhong +5
Large Reasoning Models (LRMs) often suffer from the ``over-thinking'' problem, generating unnecessarily long reasoning on simple tasks. Some strategies have been proposed to mitiga…
Exploring Spectral Characteristics for Single Image Reflection Removal
Pengbo Guo, Chengxu Liu, Guoshuai Zhao +3
Eliminating reflections caused by incident light interacting with reflective medium remains an ill-posed problem in the image restoration area. The primary challenge arises from th…
A Plug-and-play Model-agnostic Embedding Enhancement Approach for Explainable Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao +2
Existing multimedia recommender systems provide users with suggestions of media by evaluating the similarities, such as games and movies. To enhance the semantics and explainabilit…
Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao +2
Personalized federated recommendation system (FedRec) has gained significant attention for its ability to preserve privacy in delivering tailored recommendations. To alleviate the…
A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions
Yunqi Mi, Jiakui Shen, Guoshuai Zhao +2
Extending recommender systems to federated learning (FL) frameworks to protect the privacy of users or platforms while making recommendations has recently gained widespread attenti…
A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation
Jiakui Shen, Yunqi Mi, Guoshuai Zhao +2
Centralized recommender systems encounter privacy leakage due to the need to collect user behavior and other private data. Hence, federated recommender systems (FedRec) have become…
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
Haokai Ma, Yunshan Ma, Ruobing Xie +5
Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…