Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138180
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dc.contributor.authorSun, Zhuen_US
dc.contributor.authorGuo, Qingen_US
dc.contributor.authorYang, Jieen_US
dc.contributor.authorFang, Huien_US
dc.contributor.authorGuo, Guibingen_US
dc.contributor.authorZhang, Jieen_US
dc.contributor.authorBurke, Robinen_US
dc.date.accessioned2020-04-28T02:12:25Z-
dc.date.available2020-04-28T02:12:25Z-
dc.date.issued2019-
dc.identifier.citationSun, 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.2019.100879en_US
dc.identifier.issn1567-4223en_US
dc.identifier.urihttps://hdl.handle.net/10356/138180-
dc.description.abstractRecommender 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.en_US
dc.language.isoenen_US
dc.relationSLE-RP6en_US
dc.relation.ispartofElectronic Commerce Research and Applicationsen_US
dc.rights© 2019 Elsevier. All rights reserved. This paper was published in Electronic Commerce Research and Applications and is made available with permission of Elsevier.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleResearch commentary on recommendations with side information : a survey and research directionsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.elerap.2019.100879-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85072564292-
dc.identifier.volume37en_US
dc.identifier.spage100879en_US
dc.subject.keywordsSide informationen_US
dc.subject.keywordsResearch commentaryen_US
item.fulltextWith Fulltext-
item.grantfulltextembargo_20211231-
Appears in Collections:EEE Journal Articles
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  Until 2021-12-31
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