User satisfaction in long term group recommendations
In this paper we introduce our application HappyMovie, a Facebook application for movie recommendation to groups. This system takes advantage of social data available in this social network to promote fairness for the provided recommendations. Group recommendations are based in the individual satisf...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/45403 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/45403 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.8 Systems Informática (Informática) 1203.17 Informática |
| Sumario: | In this paper we introduce our application HappyMovie, a Facebook application for movie recommendation to groups. This system takes advantage of social data available in this social network to promote fairness for the provided recommendations. Group recommendations are based in the individual satisfaction of each individual. The(in)satisfaction of users modifies the typical aggregation functions used to estimate the value of an item for the group. This paper proposes a memory of past recommendations to compute the satisfaction of users when similar items (movies, in this case) are recommended several times. |
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