图 书 馆
ATT：分析社交媒体中主题和作者的时间动态ATT: Analyzing Temporal Dynamics of Topics and Authors in Social Media |
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| 课程网址: | https://videolectures.net/acmwebsci2011_naveed_topics/ |
| 主讲教师: | Nasir Naveed |
| 开课单位: | 科布伦茨-朗道大学 |
| 开课时间: | 2011-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  了解主题趋势和主题演变中的用户角色是信息检索领域的重要挑战。在这项贡献中，我们提出了一种新颖的模型，用于分析用户对制作内容随时间推移的兴趣变化。我们的方法作者主题时间模型（ATT）通过作者，潜在主题和时间信息之间的贝叶斯建模来解决此问题。我们扩展了最新的潜在Dirichlet分配（LDA）主题模型，以合并作者和时间戳信息，以捕获用户对不断发展的潜在主题随时间变化的兴趣。我们目前将该模型应用于CiteSeer的9年科学出版物数据集的结果，显示了改进的语义衔接性主题检测并捕获了与主题演变有关的作者兴趣的转移。我们还将讨论在新颖的挖掘和推荐场景中使用模型的机会。 |
| 课程简介: | Understanding Topical trends and user roles in topic evolution is an important challenge in the field of information retrieval. In this contribution, we present a novel model for analyzing evolution of user’s interests with respect to produced content over time. Our approach Author-Topic-Time model (ATT) addresses this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We extend state of the art Latent Dirichlet Allocation (LDA) topic model to incorporate the author and timestamp information for capturing changes in user interest over time with respect to evolving latent topics. We present results of application of the model to the 9 years of scientific publication datasets from CiteSeer showing improved semantically cohesive topic detection and capturing shift in authors interest in relation to topic evolution. We also discuss opportunities of model use in novel mining and recommendation scenarios. |
| 关 键 词: | 主题时间模型; 潜在Dirichlet分配; 贝叶斯建模 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-06-03：吴淑曼 |
| 最后编审: | 2020-06-11：chenxin |
| 阅读次数: | 72 |