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The ontology on which the similarity metric is based is additionally leveraged to model and reason about users\u2019 contexts; the proposed LDA model also guides the users\u2019 context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user\u2019s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users\u2019 preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).<\/jats:p>","DOI":"10.1177\/0165551517698787","type":"journal-article","created":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T11:14:57Z","timestamp":1489749297000},"page":"464-490","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":27,"title":["Towards a knowledge-based probabilistic and context-aware social recommender system"],"prefix":"10.1177","volume":"44","author":[{"given":"Luis Omar","family":"Colombo-Mendoza","sequence":"first","affiliation":[{"name":"Faculty of Informatics, University of Murcia, 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