Name
Abstract | ||
|---|---|---|
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular solutions usually suffer two key issues: 1) only focusing on feature interactions and failing to capture the sequence influence; 2) only focusing on sequence information, but ignoring internal feature relations of each event, thus failing to extract a better event representation. In this paper, we consider a two-level structure for capturing the hierarchical information over user's event sequence: 1) learning effective feature interactions based event representation; 2) modeling the sequence representation of user's historical events. Experimental results on both industrial and public datasets clearly demonstrate that our model achieves significantly better performance compared with state-of-the-art baselines.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401307 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8016-4 | 3 |
PageRank | References | Authors |
0.41 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongbo Xi | 1 | 66 | 4.02 |
| Fuzhen Zhuang | 2 | 827 | 75.28 |
| Bowen Song | 3 | 3 | 0.41 |
| Yongchun Zhu | 4 | 108 | 10.75 |
| Shuai Chen | 5 | 3 | 0.41 |
| Dan Hong | 6 | 3 | 0.41 |
| Tao Chen | 7 | 49 | 21.43 |
| Xianfeng Gu | 8 | 2997 | 189.71 |
| Qing He | 9 | 754 | 80.58 |