Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142804
Title: Deep learning based recommender system : a survey and new perspectives
Authors: Zhang, Shuai
Yao, Lina
Sun, Aixin
Tay, Yi
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system : a survey and new perspectives. ACM Computing Surveys, 52(1), 5-. doi:10.1145/3285029
Journal: ACM Computing Surveys
Abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.
URI: https://hdl.handle.net/10356/142804
ISSN: 0360-0300
DOI: 10.1145/3285029
Rights: © 2019 Association for Computing Machinery. All rights reserved. This paper was published in ACM Computing Surveys and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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