An ontology-based recommender system architecture for semantic searches in vehicles sales portals

Abstract

Internet has become an increasingly constant presence everywhere that people go. Particularly this reality is visible in social networks and selling portals scenarios. Whatever scenario, there is plenty of space to improve accuracy since big data is a problem when scale increases. Semantic search is an alternative to improve search accuracy by understanding the contextual meaning of terms as they appear in the searchable data space. Among the several approaches to Semantic Search methodologies, a variation of Ontology-based search (or Logic Approach) is the one adopted. In this methodology, the engine not only understands hierarchical relationships of entities, however also more complex inter-entities relationships defined inside ontologies. This paper proposes a hybrid approach for the problem using Ontology-based Recommender Systems and semantic profiles. A portal prototype is designed and implemented for the domain of online dealership's vehicle buyer's market. Precision and Recall measures are the two major indices of information retrieval. They have been used to evaluate the prototype results. After calculating these two metrics over some searches, we have seen that Precision is 86.66% and Recall is 68.42%. These final results have demonstrated an improvement in the searches, particularly with regard the precision of the results provided to the users. © 2014 Springer International Publishing.