An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles

• Published in: Engineering applications of artificial intelligence Vol. 24; no. 8; pp. 1385 - 1397
• Main Authors: Blanco-Fernández, Yolanda, López-Nores, Martín, Pazos-Arias, José J., García-Duque, Jorge
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2011
• Subjects: Artificial intelligence; Consumption stereotypes; Expert systems; Filtering; Filtration; Joining; Ontology; Personalization; Recommender systems; Semantic reasoning; Semantics; Strategy; Temporal logic; Time-aware filtering; Personalization; Consumption stereotypes; Semantic reasoning; Ontology; Recommender systems; Time-aware filtering
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2011.02.020

Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a user's interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches.