11 citations · 22 across the 13 of their papers we have counts for
5 papers · 1 filter
Inherit4Rec: Parameter Inheritance for Efficient Scaling of Recommendation Models
Ruihao Zhang, Bo Chen, Xiao Wang +8
Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training new models from sc…
SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation
Rui Zhou, Bo Chen, Qinglin Jia +5
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate…
UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation
Bo Chen, Jinlong Jiao, Tijian Hu +12
Recently, substantial progress has been made in industrial recommendation through component-centric model scaling, where individual components such as behavior modeling, feature in…
DIET: Learning to Distill Dataset Continually for Recommender Systems
Jiaqing Zhang, Hao Wang, Mingjia Yin +6
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, ret…
FairFS: Addressing Deep Feature Selection Biases for Recommender System
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Large-scale online marketplaces and recommender systems serve as critical technological support for e-commerce development. In industrial recommender systems, features play vital r…