A Partition and Interaction Combined Model for Social Event Popularity Prediction
文献类型：会议论文
| 作者 | Guandan Chen ; Qingchao Kong; Wenji Mao; Daniel Zeng
|
| 出版日期 | 2018 |
| 会议日期 | 2018.11.08-2018.11.10 |
| 会议地点 | Miami, FL, USA |
| 关键词 | Popularity Prediction Information Cascade Reinforcement Learning |
| 英文摘要 | Social media platforms make the spread of social event information quicker and more convenient. Some of these social events may become hot topics, which highlights the importance of event popularity prediction in public management， decision making and other security related applications. Due to the complexity of social event itself, it has two unique characteristics which most previous popularity prediction work has ignored: (1) the discussion of an event itself may consist of several components, e.g. different sub-events, different stances or different user communities; (2) the popularity of an event can be influenced by other related events. To address its unique characteristics, we propose an event popularity prediction model combining partition and interaction. We employ reinforcement learning to automatically partition an event into components and recognize related events. Then we predict event popularity by modeling component information and interactions between related events. Experimental results on a real world dataset show that our proposed model can outperform the competitive baseline methods. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21798]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 2.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 3.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 4.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Guandan Chen,Qingchao Kong,Wenji Mao,et al. A Partition and Interaction Combined Model for Social Event Popularity Prediction[C]. 见:. Miami, FL, USA. 2018.11.08-2018.11.10. |
入库方式： OAI收割
来源：自动化研究所
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