Name
Abstract | ||
|---|---|---|
Users in e-commerce tend to click on items of their interest. Eventually, the more frequently an item is clicked by a user, the more likely the item will be purchased by the user after all. However, what if a user clicked on every item only once before purchases? This is a frequently observed user behavior in reality, but predicting which of the clicked items will be purchased is a challenging task. This paper addresses a practical yet widely overlooked task of predicting purchase items within a non-duplicate click session, i.e., a session in which every item is clicked only once. We propose an encoder–decoder neural architecture to simultaneously model users’ click and purchase behaviors. The encoder captures a user’s intent contained in the user’s click session, and the decoder, which is equipped with pointer network via a switch gate, extracts relevant clicked items for future purchase candidates. To the best of our knowledge, our work is the first to address the task of purchase prediction given non-duplicate click sessions. Experiments demonstrate that our proposed method outperforms the state-of-the-art purchase prediction methods by up to 18% in terms of recall. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.knosys.2019.104932 | Knowledge-Based Systems |
Keywords | Field | DocType |
Recommender system,Purchase prediction,Sequential prediction | Pointer (computer programming),Encoder decoder,Information retrieval,Computer science,Network switch,Artificial intelligence,Encoder,Recall,Machine learning | Journal |
Volume | ISSN | Citations |
185 | 0950-7051 | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chanyoung Park | 1 | 163 | 12.04 |
| Dong Hyun Kim | 2 | 164 | 7.55 |
| Hwanjo Yu | 3 | 1715 | 114.02 |