Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1744
Title: | A Sparse Nonparametric Hierarchical Bayesian Approach Towards Inductive Transfer for Preference Modeling |
Authors: | Chatzis, Sotirios P. Demiris, Yiannis |
Major Field of Science: | Engineering and Technology |
Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering |
Keywords: | Computer science;Artificial intelligence;Expert systems (Computer science);Knowledge management;Computer multitasking;Preference learning;Nonparametric models;Multitask learning;Dirichlet process;Automatic relevance determination |
Issue Date: | 15-Jun-2012 |
Source: | Expert systems with applications, 2012, vol. 39, no. 8, pp. 7235-7246 |
Volume: | 39 |
Issue: | 8 |
Start page: | 7235 |
End page: | 7246 |
Journal: | Expert systems with applications |
Abstract: | In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely) |
URI: | https://hdl.handle.net/20.500.14279/1744 |
ISSN: | 09574174 |
DOI: | 10.1016/j.eswa.2012.01.053 |
Rights: | © 2012 Elsevier. |
Type: | Article |
Affiliation : | Imperial College London |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
CORE Recommender
Sorry the service is unavailable at the moment. Please try again later.
Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα