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A Hybrid Ontology-Based Recommendation System in e-Commerce

Coimbra Polytechnic –Instituto Superior de Engenharia de Coimbra (ISEC), 3030-190 Coimbra, Portugal
FATEC Mogi das Cruzes, São Paulo Technological College, Mogi das Cruzes 08773-600, Brazil
Centre of Informatics and Systems of University of Coimbra (CISUC), 3030-290 Coimbra, Portugal
Authors to whom correspondence should be addressed.
Algorithms 2019, 12(11), 239;
Received: 8 October 2019 / Revised: 3 November 2019 / Accepted: 5 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Algorithms for Personalization Techniques and Recommender Systems)
The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended. View Full-Text
Keywords: recommendation system; ontology; collaborative filtering; KNN; data mining recommendation system; ontology; collaborative filtering; KNN; data mining
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Guia, M.; Silva, R.R.; Bernardino, J. A Hybrid Ontology-Based Recommendation System in e-Commerce. Algorithms 2019, 12, 239.

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