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A systematic literature review of Linked Data‐based recommender systems
Author(s) -
Figueroa Cristhian,
Vagliano Iacopo,
Rocha Oscar Rodríguez,
Morisio Maurizio
Publication year - 2015
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.3449
Subject(s) - computer science , recommender system , personalization , scalability , linked data , data science , set (abstract data type) , similarity (geometry) , information retrieval , software , data mining , best practice , data publishing , systematic review , publishing , world wide web , database , semantic web , artificial intelligence , management , medline , political science , law , economics , image (mathematics) , programming language
Summary Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright © 2015 John Wiley & Sons, Ltd.