Generally 2% of shoppers make a purchase on the first visit to an online store while the other 98% enjoys only window-shopping. To bring people back to the store and close the deal, "retargeting" has been a vital online advertising strategy that leads to "conversion" of window-shoppers into buyers. As such retargeting is more effective as a focused tool, in this paper, we study the problem of identifying a conversion rate for a given product and its current customers, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customeror product-level conversion pattern, we propose a joint modeling of both level patterns based on the well-studied buying decision process. To evaluate the effectiveness of our method, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs. The evaluation results show that conversion predictions by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.
|Title of host publication||WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|Publication status||Published - 2017 Feb 2|
|Event||10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom|
Duration: 2017 Feb 6 → 2017 Feb 10
|Name||WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining|
|Other||10th ACM International Conference on Web Search and Data Mining, WSDM 2017|
|Period||17/2/6 → 17/2/10|
Bibliographical noteFunding Information:
This work was partially supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2720-16- 0007) supervised by the IITP (Institute for Information & communications Technology Promotion).
© 2017 ACM.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Information Systems
- Computer Networks and Communications