A systematic literature review of Linked Data‐based recommender systems

Abstract

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.Use URI (uniform resource identifiers) as names for things. Use HTTP (Hypertext Transfer Protocol) URIs, so that people can look up those names. Use of standard mechanisms to provide useful information when someone looks up a URI, for example, RDF (Resource Description Framework) to represent data as graphs and SPARQL (SPARQL Protocol and RDF Query Language) to query Linked Data. Include links to other URIs, so that they can discover more things.The main benefit of using Linked Data as a source for generating recommendations is the large amount of available concepts and the relationships between them that can be used to infer relations more effectively in comparison to derive the same kind of relationships from text [12]. As Linked Data information is machine-readable, it is possible to query datasets on a fine-grained level in order to collect information without having to take manual actions; therefore, information is explicitly represented, which allows for applying reasoning techniques when querying datasets and making implicit knowledge explicit. Recommender systemsRS are software tools and techniques that provide suggestions of items to a user. These items can belong to different categories or types, for example, songs, places, news, books, films, and events. ‡ 4661 According to Adomavicius and Tuzhilin [4], the roots of RS can be traced back to the works in cognitive science, approximation theory, information retrieval, forecasting theories, management science, and consumer choice modeling in marketing.Nowadays, RS are focused on the recommendation problem of guiding users in a personalized way to interesting items in a large space of possible options [10]. Typically, RS are classified as content based, collaborative filtering, knowledge based, and ...

Hybrid recommender systems: A systematic literature review

“…These results indicate that journal studies have lower spread and slightly higher quality score than conference studies. The authors in [30], a systematic review work about linked data-based recommender systems, report similar results. Regarding the publication year of the selected 25 Figure 9: RQs and higher-order themes studies, we see in Figure 2 a steady increase in hybrid RS publications.…”

Content Recommendation through Semantic Annotation of User Reviews and Linked Data

“…Linked Data based Recommender Systems. In the past, some studies reviewed different Linked Data based recommender systems that were proposed in the literature [8,11]. Typically these recommender systems consider the relationships among resources by taking into account the existing links in the Web of Data and use these relationships to measure the semantic similarity of the resources.…”

“…For the implementation, we rely on the LibRec library 11 . It computes measures according to the all unrated items protocol [25].…”

Event space model in virtual and real society based on special field

“…With era of big data and development of web data mining [4][5][6][7][8][9][10][11][12], increasing number of scholars are interested in web-based events mining [3,13]. Traditional events discovery methods often cluster similar documents together [14], including single-pass clustering methods [15][16][17].…”