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    Breaking out of the box of recommendations

    Xie, M. and Lakshmanan, L.V.S. and Wood, Peter T. (2010) Breaking out of the box of recommendations. In: Amatriain, X. and Torrens, M. and Resnick, P. and Zanker, M. (eds.) Proceedings of the fourth ACM conference on Recommender systems - RecSys '10. New York, U.S.: ACM Press, pp. 151-158. ISBN 9781605589060.

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    Abstract

    Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 11 Jun 2013 13:55
    Last Modified: 02 Dec 2016 13:26
    URI: http://eprints.bbk.ac.uk/id/eprint/7453

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