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20232026
most citedOn-Device Recommender Systems: A Comprehensive Survey

6 citations · 20 across the 21 of their papers we have counts for

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5 papers · 1 filter

cs.AI2025

AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions

Jianxin Li, Liang Qu, Taotao Cai +13

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalitie…

cs.IR2025

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation

Liang Qu, Jianxin Li, Wei Yuan +3

Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping da…

cs.IR2025

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model

Zhaofeng Zhong, Wei Yuan, Liang Qu +4

With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face t…

cs.IR2025

Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts

Yunke Qu, Liang Qu, Tong Chen +2

Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing u…

cs.IR2025

M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation

Qianru Zhang, Liang Qu, Honggang Wen +4

Sequential recommendation systems aim to predict users' next preferences based on their interaction histories, but existing approaches face critical limitations in efficiency and m…