SMOOTH START: A UNIFIED APPROACH FOR GRADUAL TRANSITION FROM COLD TO OLD IN RECOMMENDER SYSTEMS

被引：0
|
作者：
Yang, Jianwen [1 ]
Zhang, Xiao [1 ]
Xu, Jun [1 ]
机构：
[1] Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China
来源：
2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, ICASSP 2024 | 2024年
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Recommender Systems; Cold-Start;
D O I：
10.1109/ICASSP48485.2024.10447375
中图分类号：
学科分类号：
摘要：
In recommender systems, the cold-start problem poses a significant challenge, especially as users transition from being new to more engaged. Existing solutions often lack the granularity to accommodate this evolving user engagement, resulting in suboptimal performance for intermediate and older users. We introduce "Smooth Start", a unified model that addresses this oversight through a gating mechanism. This mechanism dynamically adjusts the information flow based on each user's level of engagement, providing a tailored focus for more personalized recommendations. Experimental results validate the effectiveness of "Smooth Start" across varying levels of engagement, offering a more nuanced and efficient solution to the cold-start problem.
引用
收藏
页码：5820 / 5824
页数：5
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