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
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.
2015
6th Italian Information Retrieval Workshop, IIR 2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/89498
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