Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138180
Title: Research commentary on recommendations with side information : a survey and research directions
Authors: Sun, Zhu
Guo, Qing
Yang, Jie
Fang, Hui
Guo, Guibing
Zhang, Jie
Burke, Robin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Sun, Z., Guo, Q., Yang, J., Fang, H., Guo, G., Zhang, J., & Burke, R. (2019). Research commentary on recommendations with side information : a survey and research directions. Electronic Commerce Research and Applications. Electronic Commerce Research and Applications, 37, 100879. doi:10.1016/j.elerap.2019.100879
Project: SLE-RP6
Journal: Electronic Commerce Research and Applications
Abstract: Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.
URI: https://hdl.handle.net/10356/138180
ISSN: 1567-4223
DOI: 10.1016/j.elerap.2019.100879
Rights: © 2019 Elsevier. All rights reserved. This paper was published in Electronic Commerce Research and Applications and is made available with permission of Elsevier.
Fulltext Permission: embargo_20211231
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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