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Prediction of Customer Purchase Probablity for Online Recommendation Systems
- Authors
- Han, Song-Yi; Kim, Jong Woo
- Issue Date
- Feb-2012
- Publisher
- IACSIT Press
- Keywords
- Purchase probability; Clickstream data; Online Recommendation System
- Citation
- 2012 International Conference on Information and Computer Applications, v.24, no. , pp.62 - 66
- Indexed
- OTHER
- Journal Title
- 2012 International Conference on Information and Computer Applications
- Volume
- 24
- Start Page
- 62
- End Page
- 66
- ISSN
- 2010-460X
- Abstract
- Tracking of online customers’ behavior is essential part in online recommendation systems. Specifically, for online recommendation at online stores, it is necessary to understanding customers’ purpose through their real-time activity data. This study suggests state probability methods that predict customers’ purchase probability using their clickstream data. Also, it verifies usefulness of the proposed method by using real clickstream data of an online book store. From experimental results, state probability models show better performance. Also, 2-state model with weight show best performance among proposed methods
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Related Researcher
- Kim, Jong Woo
- SCHOOL OF BUSINESS (SCHOOL OF BUSINESS ADMINISTRATION)