Generating Top-k packages via preference elicitation
Xie, M. and Lakshmanan, L.V.S. and Wood, Peter T. (2014) Generating Top-k packages via preference elicitation. Proceedings of the VLDB Endowment 7 (14), pp. 1941-1952. ISSN 2150-8097.
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Abstract
There are several applications, such as play lists of songs or movies, and shopping carts, where users are interested in finding top-k packages, consisting of sets of items. In response to this need, there has been a recent urry of activity around extending classical recommender systems (RS), which are effective at recommending individual items, to recommend packages, or sets of items. The few recent proposals for package RS suffer from one of the following drawbacks: they either rely on hard constraints which may be difficult to be specified exactly by the user or on returning Paretooptimal packages which are too numerous for the user to sift through. To overcome these limitations, we propose an alternative approach for finding personalized top-k packages for users, by capturing users' preferences over packages using a linear utility function which the system learns. Instead of asking a user to specify this function explicitly, which is unrealistic, we explicitly model the uncertainty in the utility function and propose a preference elicitation-based framework for learning the utility function through feedback provided by the user. We propose several samplingbased methods which, given user feedback, can capture the updated utility function. We develop an efficient algorithm for generating top-k packages using the learned utility function, where the rank ordering respects any of a variety of ranking semantics proposed in the literature. Through extensive experiments on both real and synthetic datasets, we demonstrate the efficiency and effectiveness of the proposed system for finding top-k packages.
Metadata
Item Type: | Article |
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School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
Research Centres and Institutes: | Birkbeck Knowledge Lab |
Depositing User: | Peter Wood |
Date Deposited: | 11 Nov 2014 09:51 |
Last Modified: | 09 Aug 2023 12:35 |
URI: | https://eprints.bbk.ac.uk/id/eprint/10941 |
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