2019 DeepLearningBasedRecommenderSys

• (Zhang, Yao et al., 2019) ⇒ Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. (2019). “Deep Learning Based Recommender System: A Survey and New Perspectives.” In: ACM Computing Surveys (CSUR) Journal, 52(1). doi:10.1145/3285029

Subject Headings: Deep Learning Based Recommener System, Deep Learning Neural Network.

Cited By

• Google Scholar: ~ 82 Citations (Retrieved:2019-10-27).
• ACM DL: ~ 21 Citations (Retrieved:2019-10-27).
• Semantic Scholar: ~ 217 Citations (Retrieved:2019-10-27).

Quotes

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2019 DeepLearningBasedRecommenderSys	Aixin Sun Shuai Zhang Lina Yao Yi Tay			Deep Learning Based Recommender System: A Survey and New Perspectives				10.1145/3285029		2019