article

CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing

Authors:

Mohsen GuizaniAuthors Info & Claims

Mobile Networks and Applications, Volume 23, Issue 6

Pages 1610 - 1623

https://doi.org/10.1007/s11036-018-1112-1

Published: 01 December 2018 Publication History

Abstract

With the explosion of social data comes a great challenge called information overloading. To overcome this challenge, recommender systems are expected to support users in quickly accessing the appropriate content. However, cold-start users are a formidable challenge in the design of recommender systems because the conventional recommendation services are based on a single data source, namely, a single field. Considering the advantages of social-based and cross-domain approaches involving further additional data, we propose a cross-domain recommender system, including three approaches, based on multi-source social big data. The proposed approach is expected to effectively alleviate the issues of cold-start users by transferring user preferences from a related auxiliary domain to a target domain. Moreover, the transferred preferences are able to improve the diversity of recommendations. Through adequate evaluations based on an actual dataset in the book and music domains, it is shown that the accuracies of the three proposed approaches are significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization. In particular, the proposed approaches are available to provide cold-start users with highly effective recommendations.

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Cited By

Wang Y(2022)The Application of Computer-Based Multimedia Technology in Cognitive ComputingComputational Intelligence and Neuroscience10.1155/2022/33545762022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3354576
Sreedevi ANitya Harshitha TSugumaran VShankar P(2022)Application of cognitive computing in healthcare, cybersecurity, big data and IoTInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10288859:2Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102888
Panda DRay S(2022)Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature reviewJournal of Intelligent Information Systems10.1007/s10844-022-00698-559:2(341-366)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10844-022-00698-5
Show More Cited By

CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

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Published In

cover image Mobile Networks and Applications

Mobile Networks and Applications Volume 23, Issue 6

December 2018

290 pages

ISSN:1383-469X

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2018

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Cited By

Wang Y(2022)The Application of Computer-Based Multimedia Technology in Cognitive ComputingComputational Intelligence and Neuroscience10.1155/2022/33545762022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3354576
Sreedevi ANitya Harshitha TSugumaran VShankar P(2022)Application of cognitive computing in healthcare, cybersecurity, big data and IoTInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10288859:2Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102888
Panda DRay S(2022)Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature reviewJournal of Intelligent Information Systems10.1007/s10844-022-00698-559:2(341-366)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10844-022-00698-5
Kamruzzaman MZhang Y(2020)Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural NetworkWireless Communications & Mobile Computing10.1155/2020/36856142020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/3685614

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