Transfer Hawkes processes with content information
Hawkes processes are widely used for modeling event cascades. However, content and cross-domain information which is also instrumental in modeling is usually neglected. In this paper, we propose a novel model called transfer Hybrid Least Square for Hawkes (trHLSH) that incorporates Hawkes processes...
| Главные авторы: | , , |
|---|---|
| Другие авторы: | |
| Формат: | Conference Paper |
| Язык: | English |
| Опубликовано: |
2020
|
| Предметы: | |
| Online-ссылка: | https://hdl.handle.net/10356/142999 |
| _version_ | 1826115419642003456 |
|---|---|
| author | Li, Tianbo Wei, Pengfei Ke, Yiping |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Li, Tianbo Wei, Pengfei Ke, Yiping |
| author_sort | Li, Tianbo |
| collection | NTU |
| description | Hawkes processes are widely used for modeling event cascades. However, content and cross-domain information which is also instrumental in modeling is usually neglected. In this paper, we propose a novel model called transfer Hybrid Least Square for Hawkes (trHLSH) that incorporates Hawkes processes with content and cross-domain information. We also present the effective learning algorithm for the model. Evaluation on both synthetic and real-world datasets demonstrates that the proposed model can jointly learn knowledge from temporal, content and cross-domain information, and has better performance in terms of network recovery and prediction. |
| first_indexed | 2024-10-01T03:55:10Z |
| format | Conference Paper |
| id | ntu-10356/142999 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:55:10Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1429992020-07-20T08:18:41Z Transfer Hawkes processes with content information Li, Tianbo Wei, Pengfei Ke, Yiping School of Computer Science and Engineering 2018 IEEE International Conference on Data Mining (ICDM) Centre for Computational Intelligence Engineering::Computer science and engineering::Computer applications Hawkes Processes Transfer Learning Hawkes processes are widely used for modeling event cascades. However, content and cross-domain information which is also instrumental in modeling is usually neglected. In this paper, we propose a novel model called transfer Hybrid Least Square for Hawkes (trHLSH) that incorporates Hawkes processes with content and cross-domain information. We also present the effective learning algorithm for the model. Evaluation on both synthetic and real-world datasets demonstrates that the proposed model can jointly learn knowledge from temporal, content and cross-domain information, and has better performance in terms of network recovery and prediction. MOE (Min. of Education, S’pore) Accepted version 2020-07-20T08:18:41Z 2020-07-20T08:18:41Z 2018 Conference Paper Li, T., Wei, P., & Ke, Y. (2018). Transfer Hawkes processes with content information. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), 1116-1121. doi:10.1109/icdm.2018.00145 978-1-5386-9160-1 https://hdl.handle.net/10356/142999 10.1109/ICDM.2018.00145 1116 1121 en © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICDM.2018.00145 application/pdf |
| spellingShingle | Engineering::Computer science and engineering::Computer applications Hawkes Processes Transfer Learning Li, Tianbo Wei, Pengfei Ke, Yiping Transfer Hawkes processes with content information |
| title | Transfer Hawkes processes with content information |
| title_full | Transfer Hawkes processes with content information |
| title_fullStr | Transfer Hawkes processes with content information |
| title_full_unstemmed | Transfer Hawkes processes with content information |
| title_short | Transfer Hawkes processes with content information |
| title_sort | transfer hawkes processes with content information |
| topic | Engineering::Computer science and engineering::Computer applications Hawkes Processes Transfer Learning |
| url | https://hdl.handle.net/10356/142999 |
| work_keys_str_mv | AT litianbo transferhawkesprocesseswithcontentinformation AT weipengfei transferhawkesprocesseswithcontentinformation AT keyiping transferhawkesprocesseswithcontentinformation |