Paper Info
Title
Generating top-k packages via preference elicitation
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 flurry 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 Pareto-optimal 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 sampling-based 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.
Year
DOI
Venue
2014
10.14778/2733085.2733099
PVLDB
Field
DocType
Volume
Recommender system,Data mining,Scale-invariant feature transform,Preference elicitation,Ranking,Computer science,Sampling (statistics),Database,Semantics
Journal
7
Issue
ISSN
Citations 
14
2150-8097
11
PageRank 
References 
Authors
0.58
21
3
Name
Order
Citations
PageRank
Min Xie121711.60
Laks V. S. Lakshmanan26216696.78
Peter T. Wood31238249.44