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Enhanced similarity measure for personalized cloud services recommendation
Author(s) -
Afify Y.M.,
Moawad I.F.,
Badr N.L.,
Tolba M.F.
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4020
Subject(s) - computer science , cloud computing , recommender system , collaborative filtering , similarity (geometry) , similarity measure , key (lock) , information retrieval , measure (data warehouse) , process (computing) , service (business) , data mining , set (abstract data type) , world wide web , artificial intelligence , computer security , economy , economics , image (mathematics) , programming language , operating system
Summary 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.