Generating Recommendations for Consensus Negotiations in Group Personalization Services
There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to recommender systems that have the ability to pr...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2012 |
| País: | España |
| Recursos: | Universidad de Barcelona |
| Repositório: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/124911 |
| Acesso em linha: | https://hdl.handle.net/2445/124911 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Intel·ligència artificial Sistemes d'ajuda a la decisió Artificial intelligence Decision support systems |
| Resumo: | There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to recommender systems that have the ability to provide even the most tentative shoppers with compelling and timely item suggestions. When the recommendations are made for a group of users, new challenges and issues arise to provide compelling item suggestions. One of the challenges a group recommender system must cope with is the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper, we focus on how individual user models can be aggregated to reach a consensus on recommendations. We describe and evaluate nine different consensus strategies and analyze them to highlight the benefits of group recommendation using live-user preference data. Moreover, we show that the performance is significantly different among strategies. |
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