A systematic literature review of Linked Data-based recommender systems

摘要

Recommender systems (RS) are software tools that use analytic technologies to suggest different items ofrninterest to an end user. Linked Data is a set of best practices for publishing and connecting structured data onrnthe Web. This paper presents a systematic literature review to summarize the state of the art in RS that usernstructured data published as Linked Data for providing recommendations of items from diverse domains. Itrnconsiders the most relevant research problems addressed and classifies RS according to how Linked Datarnhave been used to provide recommendations. Furthermore, it analyzes contributions, limitations, applicationrndomains, evaluation techniques, and directions proposed for future research. We found that there are stillrnmany open challenges with regard to RS based on Linked Data in order to be efficient for real applications.rnThe main ones are personalization of recommendations, use of more datasets considering the heterogeneityrnintroduced, creation of new hybrid RS for adding information, definition of more advanced similarity measuresrnthat take into account the large amount of data in Linked Data datasets, and implementation of testbedsrnto study evaluation techniques and to assess the accuracy scalability and computational complexity of RS.