2 citations · 6 across the 5 of their papers we have counts for
5 papers
IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems
Xinchun Li, Ning Zhang, Qianqian Yang +11
Although sophisticated sequence modeling paradigms have achieved remarkable success in recommender systems, the information capacity of hand-crafted sequential features constrains…
Task-Aware Machine Unlearning and Its Application in Load Forecasting
Wangkun Xu, Fei Teng
Data privacy and security have become a non-negligible factor in load forecasting. Previous researches mainly focus on training stage enhancement. However, once the model is traine…
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Congcong Liu, Fei Teng, Xiwei Zhao +3
Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data…
Federated Graph-based Networks with Shared Embedding
Tianyi Yu, Pei Lai, Fei Teng
Nowadays, user privacy is becoming an issue that cannot be bypassed for system developers, especially for that of web applications where data can be easily transferred through inte…
On the Adaptation to Concept Drift for CTR Prediction
Congcong Liu, Yuejiang Li, Fei Teng +5
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with…