Name
Abstract | ||
|---|---|---|
Discovering interesting behavior patterns and profiles of users as they interact with E-commerce (EC) sites is an important task for site managers. We propose a probabilistic behavior model for extracting latent classes of items that impact the users' item selections but cannot be inferred from the current knowledge of the managers. The proposed model assumes that the current knowledge is represented by categories of items that are defined in the EC site, and a user selects items depending on both of their categories and latent classes. By estimating latent classes, each of which shows items accessed by users with common interests, we can find interesting factors for explaining user behavior. We evaluate our proposed model using item-access log data observed in an EC site. The results show that our model can accurately predict users' item selection, and actually discover latent classes of items having similar latent characteristic such as "colored design" and "impression" by using item categories such as "coat" and "hat" as the current knowledge of the managers. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDM.2013.65 | 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) |
Keywords | Field | DocType |
topic model, behavir model | Kernel (linear algebra),Site manager,Data modeling,Data mining,Colored,Computer science,Artificial intelligence,Probabilistic latent semantic analysis,Topic model,Probabilistic logic,Machine learning | Conference |
ISSN | Citations | PageRank |
1550-4786 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takeshi Kurashima | 1 | 315 | 24.21 |
| Tomoharu Iwata | 2 | 824 | 65.87 |
| Noriko Takaya | 3 | 48 | 2.87 |
| Hiroshi Sawada | 4 | 1809 | 136.96 |