Enhanced similarity measure for personalized cloud services recommendation
Afify, Yasmine M.; Moawad I.; Badr N.; Tolba M.;
Abstract
Copyright © 2016 John Wiley & Sons, Ltd. Cloud users are overwhelmed with great numbers of cloud services. Service recommender systems evaluate the services that provide same functionalities according to the user requirements. A key enabler to accurate recommendation in recommender systems is the appropriate determination of similar users. This paper contributes to the personalized cloud services recommendation area. In specific, we introduce a user-based similarity measure that integrates relevant similarity aspects: user demographic information, service ratings, and user interest. The proposed similarity measure is used in a hybrid collaborative filtering (CF) approach that leverages the advantages of both model- and memory-based approaches to improve the recommendation process. Experimental evaluation on real-world services data set shows that the proposed approach outperforms other CF approaches in respect of the prediction accuracy and recommendation time while maintaining better or same coverage.
Other data
Title | Enhanced similarity measure for personalized cloud services recommendation | Authors | Afify, Yasmine M. ; Moawad I.; Badr N.; Tolba M. | Issue Date | 25-Apr-2017 | Journal | Concurrency Computation | DOI | https://api.elsevier.com/content/abstract/scopus_id/84992451588 10.1002/cpe.4020 |
Scopus ID | 2-s2.0-84992451588 |
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