This paper covers a sales forecasting problem on e-commerce sites. To predict product sales, we need to understand customers' browsing behavior and identify whether it is for purchase purpose or not. For this goal, we propose a new customer model, B2P, of aggregating predictive features extracted from customers' browsing history. We perform experiments on a real world e-commerce site and show that sales predictions by our model are consistently more accurate than those by existing state-of-the-art baselines.
|Title of host publication||WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 2016 Apr 11|
|Event||25th International Conference on World Wide Web, WWW 2016 - Montreal, Canada|
Duration: 2016 May 11 → 2016 May 15
|Name||WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web|
|Conference||25th International Conference on World Wide Web, WWW 2016|
|Period||16/5/11 → 16/5/15|
Bibliographical notePublisher Copyright:
© 2016 owner/author(s).
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications