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Composite recommendations: from items to packages

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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 the 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 systems focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of decidingwhether there is a recommendation (package)whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommendations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.

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Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, Vancouver, V6T 1Z4, Canada

    Min Xie & Laks V. S. Lakshmanan

  2. Department of Computer Science and Information Systems, Birkbeck, University of London, London, WC1E 7HX, UK

    Peter T. Wood

Authors
  1. Min Xie
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  2. Laks V. S. Lakshmanan
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  3. Peter T. Wood
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Corresponding author

Correspondence to Min Xie.

Additional information

Min Xie is a PhD candidate in the Department of Computer Science at University of British Columbia. His supervisor is professor Laks V. S. Lakshmanan and he is a member of the Data Management and Mining Lab. Before coming to Vancouver, he received his bachelor and master degrees from Renmin University of China. His current research interests include database, data mining, personalization and recommender systems.

Laks V. S. Lakshmanan is a professor in the Department of Computer Science at UBC. Laks has published extensively in data management and mining and has served on the PC of all major database and data mining conferences including SIGMOD, PODS, VLDB, ICDE, KDD, and ICDM as member or Vice Chair or General co-Chair. He is an Associate Editor of the VLDB Journal. His current research interests include social networks and media, personalization and recommender systems.

Peter T. Wood obtained his BSc and MSc in Computer Science from the University of Cape Town (UCT), South Africa. He then worked for the Institute for Applied Computer Science at the University of Stellenbosch, before leaving for the University of Toronto where he obtained a PhD in Computer Science in 1989. He subsequently spent nine years as a member of the Department of Computer Science at UCT, before joining the Department of Computer Science at King’s College London in 1998. He moved to the Department of Computer Science and Information Systems at Birkbeck in 2001. His main interest has been in languages for querying data: their design, computational complexity, and optimisation.

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Xie, M., Lakshmanan, L.V.S. & Wood, P.T. Composite recommendations: from items to packages. Front. Comput. Sci. 6, 264–277 (2012). https://doi.org/10.1007/s11704-012-2014-1

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  • DOI: https://doi.org/10.1007/s11704-012-2014-1

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