Paper Recommendation with Item-Level Collaborative Memory Network
Authors: 
Mei Yu, Yue Hu, Xuewei Li, Mankun Zhao, Tianyi Xu, Hongwei Liu, Linying Xu, Ruiguo YuAuthors Info & Claims
Pages 141 - 152
Abstract
The recommendation system can recommend information to users personally and efficiently, which satisfies the user’s demand for information in the information age, and has become a hot topic in the current era. In the recommendation system, users and items and the interaction of their own information has a crucial impact on the efficiency and accuracy of the recommendations. However, most of the existing recommendation systems usually design the systems as user-base only, considering the user’s influence on the item in the recommendation, which to some extent blurs the interaction between items and users at the item level, unknown and potential connections between items and users are not well considered. In this paper, we propose a collaborative memory network that can focus on the potential relation between items and users, and consider the impact of items’ characteristics on user behavior. Experiments have shown that our improvement is better than the original method and other baseline models.
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Information
Published In
Aug 2019
867 pages
ISBN:978-3-030-29550-9
DOI:10.1007/978-3-030-29551-6
© Springer Nature Switzerland AG 2019.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 28 August 2019
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