11 citations · 22 across the 13 of their papers we have counts for
11 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…
TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance.…
Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
Nuo Chen, Quanyu Dai, Xiaoyu Dong +5
Conversational recommender systems (CRSs) integrate both recommendation and dialogue tasks, making their evaluation uniquely challenging. Existing approaches primarily assess CRS p…
RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
Zhuo Wu, Qinglin Jia, Chuhan Wu +4
Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm e…
Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation
Yuang Zhao, Zhaocheng Du, Qinglin Jia +3
With the increase in the business scale and number of domains in online advertising, multi-domain ad recommendation has become a mainstream solution in the industry. The core of mu…
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Yifan Wang, Peijie Sun, Min Zhang +3
Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retra…
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Chuhan Wu, Jingjie Li, Qinglin Jia +3
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of cons…