2 citations · 6 across the 5 of their papers we have counts for
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cs.IR2026
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…
cs.IR2023★ 1 cited
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…
cs.IR2022★ 2 cited
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…