Diversity in a recommendation list has been recognized as one of the key factors to increase user's satisfaction when interacting with a recommender system. Analogously to the modelling and exploitation of query intent in Information Retrieval adopted to improve diversity in search results, in this paper we focus on eliciting and using the profile of a user which is in turn exploited to represent her intents. The model is based on regression trees and is used to improve personalized diversification of the recommendation list in a multi-attribute setting. We tested the proposed approach and showed its effectiveness in two different domains, i.e. books and movies.
Exploiting regression trees as user models for intent-aware multi-attribute diversity / Tomeo, Paolo; Di Noia, Tommaso; De Gemmis, Marco; Lops, Pasquale; Semeraro, Giovanni; Di Sciascio, Eugenio. - 1448:(2015), pp. 2-9. (Intervento presentato al convegno 2nd Workshop on New Trends on Content-Based Recommender Systems, CBRecSys 2015 tenutosi a Vienna, Austria nel September 16-20, 2015).
Exploiting regression trees as user models for intent-aware multi-attribute diversity
Tomeo, Paolo;Di Noia, Tommaso;Di Sciascio, Eugenio
2015-01-01
Abstract
Diversity in a recommendation list has been recognized as one of the key factors to increase user's satisfaction when interacting with a recommender system. Analogously to the modelling and exploitation of query intent in Information Retrieval adopted to improve diversity in search results, in this paper we focus on eliciting and using the profile of a user which is in turn exploited to represent her intents. The model is based on regression trees and is used to improve personalized diversification of the recommendation list in a multi-attribute setting. We tested the proposed approach and showed its effectiveness in two different domains, i.e. books and movies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
