Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6800

Recommendation for custom product via probabilistic relevance model

Authors Wang, Y.
Tseng, M.M.
Issue Date 2009
Source IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management , 2009, p. 1548-1552
Summary Product recommendation system has been widely used in industry especially for e-Commerce companies to solve the problem of information overload. Nonetheless, information overload is also a severe issue in custom product development practice. Sometimes customers can easily get overwhelmed by the vast number of product varieties and it is hard for them to make choices. However, the established product recommendation approaches are primarily for off-the-shelf products, adaptation for custom products has been difficult due to the different scenarios of custom product design. In this paper, a new recommendation method for custom product design is proposed based on probabilistic relevance model. The idea is to calculate the probability that each product meets an active customer's specifications based on partial product specifications given by the customer. Then the recommendation is presented according to the ranking of probabilities of relevance. Experiments are carried out and the result shows that the presented approach can improve the recommendation efficiency significantly comparing with random recommendation. © 2009 IEEE.
Subjects
ISBN 978-1-4244-4869-2
Rights © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language English
Format Conference paper
Access View full-text via DOI
View full-text via Scopus
View full-text via Web of Science
Find@HKUST
Files in this item:
File Description Size Format
recommendation.pdf 664192 B Adobe PDF