With the convergence of social networks and recommender systems, new algorithms have been explored, that combine observations on item selection with an analysis of social relations among people. However, they focus on direct friendship relations. In this paper, concerning collaborative filtering approaches, we test the impact of integrating a measure of common friendship, in order to capture the intuition that socially related groups of people tend to have similar tastes. An experiment on the Yelp dataset shows that using preference information derived from the commonalities of interests in networks of friends achieves higher accuracy than item-to-item and other collaborative filtering algorithms.

Enhancing Collaborative Filtering with Friendship Information

ARDISSONO, Liliana;PETRONE, GIOVANNA;SEGNAN, MARINO
2017-01-01

Abstract

With the convergence of social networks and recommender systems, new algorithms have been explored, that combine observations on item selection with an analysis of social relations among people. However, they focus on direct friendship relations. In this paper, concerning collaborative filtering approaches, we test the impact of integrating a measure of common friendship, in order to capture the intuition that socially related groups of people tend to have similar tastes. An experiment on the Yelp dataset shows that using preference information derived from the commonalities of interests in networks of friends achieves higher accuracy than item-to-item and other collaborative filtering algorithms.
2017
25th Conf. on User Modeling, Adaptation and Personalization (UMAP 2017)
Bratislava, Slovakia
9-12 July 2017
Proc. of UMAP 2017
ACM
353
354
978-1-4503-4635-1
Ardissono, Liliana; Ferrero, Maurizio; Petrone, Giovanna; Segnan, Marino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1633946
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