Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering |
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Affiliation: | 1. Department of Computer Science and Artificial Intelligence, University of Granada, Calle Periodista Daniel Saucedo Aranda s/n, Granada 18071, Spain;2. School of Software, University of Technology Sydney, PO Box 123, Broadway, Ultimo, NSW 2007, Australia;3. University of Ciego de Ávila, Carretera a Morón Km. 9 1/2, Ciego de Ávila, Cuba;4. Computer Science Department, University of Jaén, Campus Las Lagunillas s/n, Jaén 23008, Spain;5. School of Management, Wuhan University of Technology, Wuhan 430070, China;1. Data Science and Big Data Analytics, Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain;2. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy;1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China;2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China;3. Shandong Normal University, Jinan 250014, China;4. Data Science and Machine Intelligence Lab, Advanced Analytics Institute, University of Technology Sydney, Australia |
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Abstract: | 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. |
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Keywords: | Group modeling Group recommendation Clustering Collaborative filtering |
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