Name
Abstract | ||
|---|---|---|
Users' online behavior, generated from endpoints of e-commerce website and app, is regarded as big data which can create large business value by mining them to acquire insights of users' preference, inclination and purpose. A good ranking result of product search and product assortment in online category classification can lift user's click rate, increase purchasing conversion rate and improve customer online experience. In this paper, we will introduce an online product based learning to rank model to intelligently learn product ranking. A big data architecture will also be introduced to implement this learning to rank model which analyzes huge amount of users' online behavior. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SOLI.2016.7551685 | 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) |
Keywords | DocType | ISBN |
Big Data based product ranking solution,user online behavior,e-commerce Web site,e-commerce application,large business value,data mining,product search,product assortment,online category classification,user click rate,purchasing conversion rate,customer online experience,online product based learning,Big Data architecture | Conference | 978-1-5090-2928-0 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinfeng Li | 1 | 30 | 4.63 |
| Bing Shao | 2 | 16 | 6.11 |
| Jian Xu | 3 | 21 | 10.93 |
| Hongliang Li | 4 | 0 | 0.34 |
| Qinghua Wang | 5 | 0 | 0.34 |