Purchase Prediction Based on a Non-parametric Bayesian Method
Files
Date
2019-01-08
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Predicting customer’s next purchase is of paramount importance for online retailers. In this paper, we present a new purchase prediction method to predict customer behavior based on non-parametric Bayesian framework. The proposed method is inspired by topic modeling for text mining. Unlike the conventional methods, we regard customer’s purchase as the result of motivations and automatically determine the number of user purchase motivations. Given customer’s purchase history, we show that customer’s next purchase can be predicted by non-parametric Bayesian model. We apply the model to real-world dataset from Amazon.com and prove it outperforms the traditional methods. Besides that, the proposed method can also determine the number of the motivations owned by users automatically, rendering it a promising approach with a good scalability.
Description
Keywords
Decision Support for Smart Cities, Decision Analytics, Mobile Services, and Service Science, purchase prediction,non-parametric bayesian,HDP
Citation
Extent
9 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 52nd Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.