Trust-based recommender systems usually overlook the cultural background of people when making suggestions. In this paper, we propose some strategies to include the home country of users in trust-based recommendation algorithms and we aim to understand if this information can improve the recommender system performance.

Impact of Users' Cultural Background on Multi-faceted Trust-based Recommender Systems

Mauro Noemi;Hu Zhongli Filippo;Petrone Giovanna;Segnan Marino;Mattutino Claudio
2023-01-01

Abstract

Trust-based recommender systems usually overlook the cultural background of people when making suggestions. In this paper, we propose some strategies to include the home country of users in trust-based recommendation algorithms and we aim to understand if this information can improve the recommender system performance.
2023
3rd Workshop on Social and Cultural Integration with Personalized Interfaces (SOCIALIZE)
Sydney, Australia
2023
Joint Workshops of IUI 2023: HAI-GEN - Human-AI Co-Creation with Generative Models, ITAH - Workshop on Interactive Technologies for AI in Healthcare, MILC - Workshop on Intelligent Music Interfaces for Listening and Creation, SHAI - Workshop on Designing for Safety in Human-AI Interactions, SketchRec - Workshop on Sketch Recognition and SOCIALIZE - Social and Cultural Integration with Personalized Interfaces
CEUR-WS
3359
246
249
Cultural Background of Users; Multi-faceted Reputation Model; Recommender systems; Social Relations; Trust-based Recommender Systems; Web searching and information discovery
Mauro Noemi; Hu Zhongli Filippo; Petrone Giovanna; Segnan Marino; Mattutino Claudio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1897832
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