Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, in order to be effectively used, several problems have to be addressed: user preferences are not expressed as rating, items to be suggested are located in a physical space, and users may be in a group. In this work, we present a general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. To reach a high-quality initial personalization, the recommendation system uses a simple, but efficient, elicitation method that is inspired by the MF approach. Moreover, the proposed approach considers the individual or the aggregated artworks’ ratings to build up a solution that takes into account the physical location of the artworks.

Towards a Collaborative Filtering Framework for Recommendation in Museums: From Preference Elicitation to Group's Visits / Rossi, Silvia; Barile, Francesco; Improta, Davide; Russo, Luca. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 58:(2016), pp. 431-436. [10.1016/j.procs.2016.09.067]

Towards a Collaborative Filtering Framework for Recommendation in Museums: From Preference Elicitation to Group's Visits

ROSSI, SILVIA;Barile, Francesco;
2016

Abstract

Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, in order to be effectively used, several problems have to be addressed: user preferences are not expressed as rating, items to be suggested are located in a physical space, and users may be in a group. In this work, we present a general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. To reach a high-quality initial personalization, the recommendation system uses a simple, but efficient, elicitation method that is inspired by the MF approach. Moreover, the proposed approach considers the individual or the aggregated artworks’ ratings to build up a solution that takes into account the physical location of the artworks.
2016
Towards a Collaborative Filtering Framework for Recommendation in Museums: From Preference Elicitation to Group's Visits / Rossi, Silvia; Barile, Francesco; Improta, Davide; Russo, Luca. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 58:(2016), pp. 431-436. [10.1016/j.procs.2016.09.067]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/650692
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