GreedyBoost: An Accurate, Efficient and Flexible Ensemble Method for B2B Recommendations

Date
2017-01-04
Authors
Zhang, Weipeng
Enders, Tobias
Li, Dongsheng
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Recommender systems have achieved great success in finding relevant products and services for individual customers, e.g. in B2C markets, during recent years. \ However, due to the diversity of enterprise clients' requirements it is still an open question on how to successfully apply existing recommendation techniques in the B2B domain. \ \ This paper presents GreedyBoost --- an accurate, efficient and flexible ensemble method for product and service recommendations in the B2B domain. Given a set of base models, GreedyBoost can sequentially add base models to the ensemble by a linear approach to minimize training error, so that the ensemble process is efficient. Meanwhile, GreedyBoost does not have any special requirement on base models and evaluation metrics, so that any kind of client requirements and sale \\& distribution purposes can be adapted. Experimental results on real-world B2B data demonstrate that GreedyBoost can achieve higher recommendation accuracy compared with two popular ensemble methods.
Description
Keywords
B2B, ensemble, optimization, recommendation, recommender system
Citation
Extent
8 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 50th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.