图 书 馆
基于抗噪时变因子图的社会行为跟踪Social Action Tracking via Noise Tolerant Time-varying Factor Graphs |
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| 课程网址: | http://videolectures.net/kdd2010_tan_sat/ |
| 主讲教师: | Chenhao Tan |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It is well known that users' behaviors (actions) in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few publications systematically study how social actions evolve in a dynamic social network and to what extent different factors affect the user actions. In this paper, we propose a Noise Tolerant Time-varying Factor Graph Model (NTT-FGM) for modeling and predicting social actions. NTT-FGM simultaneously models social network structure, user attributes and user action history for better prediction of the users' future actions. More specifically, a user's action at time t is generated by her latent state at t, which is influenced by her attributes, her own latent state at time t - 1 and her neighbors' states at time t and t - 1. Based on this intuition, we formalize the social action tracking problem using the NTT-FGM model; then present an efficient algorithm to learn the model, by combining the ideas from both continuous linear system and Markov random field. Finally, we present a case study of our model on predicting future social actions. We validate the model on three different types of real-world data sets. Qualitatively, our model can uncover some interesting patterns of the social dynamics. Quantitatively, experimental results show that the proposed method outperforms several baseline methods for action prediction. |
| 关 键 词: | 用户行为; 动态社交; 变因子图 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：cwx |
| 阅读次数: | 53 |