文章基本信息

标题：Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems
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作者：Manuel Pozo ; Raja Chiky ; Farid Meziane 等
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2018
卷号：5
期号：3
页码：35-53
DOI：10.3390/informatics5030035
出版社：MDPI Publishing
摘要：This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues.
关键词：cold start; recommender systems; active learning; collaborative filtering cold start ; recommender systems ; active learning ; collaborative filtering