An Architecture and Functional Description to Integrate Social Behaviour Knowledge Into Group Recommender Systems
In this paper we consider the research challenges of generating a set of recommendations that will satisfy a group of users, with potentially competing interests. We review diferent ways of combining the preferences of diferent users and propose an approach that takes into account the social behavio...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | español |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/34761 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/34761 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004.738.52:338.46 Group Recommender systems Social Networks Personality Trust Generic Architecture Informática (Informática) Inteligencia artificial (Informática) 1203.17 Informática 1203.04 Inteligencia Artificial |
| Resumo: | In this paper we consider the research challenges of generating a set of recommendations that will satisfy a group of users, with potentially competing interests. We review diferent ways of combining the preferences of diferent users and propose an approach that takes into account the social behaviour within a group. Our method, named delegation-based prediction method, includes an analysis of the group characteristics, such as size, structure, personality of its members in conict situations, and trust between group members. A key element in this paper is the use of social information available in the Web to make enhanced recommendations to groups. We propose a generic architecture named arise (Architecture for Recommendations Including Social Elements) and describe, as a case study, our Facebook application HappyMovie: a group recommender system that is designed to provide assistance to a group of friends that might be selecting which movie to watch on a cinema outing. We evaluate the performance (compared with the real group decision) of diferent recommenders that use increasing levels of social behaviour knowledge. |
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