5 papers
MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
Qihang Yu, Kairui Fu, Zhaocheng Du +10
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both i…
PI2I: A Personalized Item-Based Collaborative Filtering Retrieval Framework
Shaoqing Wang, Yingcai Ma, Kairui Fu +4
Efficiently selecting relevant content from vast candidate pools is a critical challenge in modern recommender systems. Traditional methods, such as item-to-item collaborative filt…
CHORD: Customizing Hybrid-precision On-device Model for Sequential Recommendation with Device-cloud Collaboration
Tianqi Liu, Kairui Fu, Shengyu Zhang +5
With the advancement of mobile device capabilities, deploying reranking models directly on devices has become feasible, enabling real-time contextual recommendations. When migratin…
Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editing
Zheqi Lv, Wenqiao Zhang, Kairui Fu +6
The on-device real-time data distribution shift on devices challenges the generalization of lightweight on-device models. This critical issue is often overlooked in current researc…
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
Kairui Fu, Shengyu Zhang, Zheqi Lv +2
Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile reques…