Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 64, Issue -, Pages 165-174Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.future.2015.10.007
Keywords
Group modeling; Group recommendation; Clustering; Collaborative filtering
Categories
Funding
- Regione Sardegna under project SocialGlue, through PIA-Pacchetti Integrati di Agevolazione Industria Artigianato e Servizi
- MIUR PRIN under project Security Horizons
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There are types of information systems, like those that produce group recommendations or a market segmentation, in which it is necessary to aggregate big amounts of data about a group of users in order to filter the data. Group modeling is the process that combines multiple user models into a single model that represents the knowledge available about the preferences of the users in a group. In group recommendation, group modeling allows a system to derive a group preference for each item. Different strategies lead to completely different group models, so the strategy used to model a group has to be evaluated in the domain in which the group recommender system operates. This paper evaluates group modeling strategies in a group recommendation scenario in which groups are detected by clustering the users. Once users are clustered and groups are formed, different strategies are tested, in order to find the one that allows a group recommender system to get the best accuracy. Experimental results show that the strategy used to build the group models strongly affects the performance of a group recommender system. An interesting property derived by our study is that clustering and group modeling are strongly connected. Indeed, the modeling strategy takes the same role that the centroid has when users are clustered, by producing group preferences that are equally distant from the preferences of every user. This continuity among the two tasks is essential in order to build accurate group recommendations. (C) 2015 Elsevier B.V. All rights reserved.
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