Building Representative Composite Items

Abstract : The problem of summarizing a large collection of homogeneous items has been addressed extensively in particular in the case of geo-tagged datasets (e.g. Flickr photos and tags). In our work, we study the problem of summarizing large collections of heterogeneous items. For example, a user planning to spend extended periods of time in a given city would be interested in seeing a map of that city with item summaries in different geographic areas, each containing a theater, a gym, a bakery, a few restaurants and a subway station. We propose to solve that problem by building representative Composite Items (CIs). To the best of our knowledge, this is the first work that addresses the problem of finding representative CIs for heterogeneous items. Our problem naturally arises when summarizing geo-tagged datasets but also in other datasets such as movie or music summarization. We formalize building representative CIs as an optimization problem and propose KFC, an extended fuzzy clustering algorithm to solve it. We show that KFC converges and run extensive experiments on a variety of real datasets that validate its effectiveness.
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Vincent Leroy, Sihem Amer-Yahia, Eric Gaussier, Hamid Mirisaee. Building Representative Composite Items. Conference on Information and Knowledge Management (CIKM) 2015, Oct 2015, Melbourne, Australia. 2015, Proceedings of the 24th ACM Conference on Information and Knowledge Management (CIKM), 2015. 〈10.1145/2806416.2806465〉. 〈hal-01180167〉

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