The evaluation of a recommendation engine cannot rely only on the accuracy of provided recommendations. One should consider ad- ditional dimensions, such as diversity of provided suggestions, in order to guarantee heterogeneity in the recommendation list. In this paper we analyse users' propensity in selecting diverse items, by taking into account content-based item attributes. Individual propensity to diversi- fication is used to re-rank the list of Top-N items predicted by a rec- ommendation algorithm, with the aim of fostering diversity in the final ranking. We show experimental results that confirm the validity of our modelling approach.
Adaptive Diversity in Recommender Systems / DI NOIA, Tommaso; Ostuni, Vito Claudio; Rosati, Jessica; Tomeo, Paolo; DI SCIASCIO, Eugenio. - 1404:(2015). (Intervento presentato al convegno 6th Italian Information Retrieval Workshop, IIR 2015 tenutosi a Cagliari, Italy nel May 25-26, 2015).
Adaptive Diversity in Recommender Systems
DI NOIA, Tommaso;OSTUNI, Vito Claudio;ROSATI, JESSICA;TOMEO, Paolo;DI SCIASCIO, Eugenio
2015-01-01
Abstract
The evaluation of a recommendation engine cannot rely only on the accuracy of provided recommendations. One should consider ad- ditional dimensions, such as diversity of provided suggestions, in order to guarantee heterogeneity in the recommendation list. In this paper we analyse users' propensity in selecting diverse items, by taking into account content-based item attributes. Individual propensity to diversi- fication is used to re-rank the list of Top-N items predicted by a rec- ommendation algorithm, with the aim of fostering diversity in the final ranking. We show experimental results that confirm the validity of our modelling approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
