BlindDate recommender: A context-aware ontology-based dating recommendation platform. (October 2019)

• Record Type: Journal Article
• Authors: Rodríguez-García, Miguel Ángel; Valencia-García, Rafael; Colomo-Palacios, Ricardo; Gómez-Berbís, Juan Miguel
• Is Part Of: Journal of information science. Volume 45:Number 5(2019)
• Page Start: 573
• Page End: 591
• Publication Date: 2019-10
• Subjects: Ontology -- recommender systems -- social recommendation -- user modelling; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551518806114
• Languages: English
• ISSNs: 0165-5515

Abstract

Online dating sites have become popular platforms for those individuals who utilise the Internet to develop a personal or romantic relationship. Unlike typical recommenders systems, which attempt to suggest items such as films, songs, books and so on. According to a user's interests, dating recommender systems provide services that people can use to find potential romantic partners. Since these services have a higher expectancy of users, online dating sites are considering the introduction of recommender systems in order to build an improved dating network. Different kinds of techniques based on content-based, collaborative filtering or hybrid techniques exist. In this article, we introduce BlindDate recommender, a context-based platform that utilises semantic technologies to describe users' preferences more precisely. We utilise DBPedia repositories to obtain information that is subsequently used to enrich a previously generated ontology model. The instances inserted into the ontology enable the matching algorithms that we have generated to identify potential matches between users. In order to validate the performance of the platform, we utilise a real-world data set that has produced relevant results enhancing the accuracy compared with other well-known approaches and identifying the discriminant parameters used in the dating domain. More specifically, the proposed approach attains 0.79, 0.8 and 0.55 in the I-Precision, I-Recall and I-F-measure, respectively, when employed in separate topics.