0


在社会化媒体的主题和作者的时空动态分析

ATT: Analyzing Temporal Dynamics of Topics and Authors in Social Media
课程网址: http://videolectures.net/acmwebsci2011_naveed_topics/  
主讲教师: Nasir Naveed
开课单位: 科布伦茨兰道大学
开课时间: 2011-07-19
课程语种: 英语
中文简介:
在信息检索领域,了解话题发展趋势和用户角色是一个重要的挑战。在这篇文章中,我们提出了一个新的模型,用于分析用户的兴趣随着时间的推移而发生的变化。作者-话题-时间模型(ATT)通过对作者、潜在话题和时间信息之间的关系进行贝叶斯建模来解决这一问题。我们扩展了最先进的潜在Dirichlet分配(LDA)主题模型,以包含作者和时间戳信息,用于捕捉用户兴趣随时间变化的潜在主题。我们展示了该模型在CiteSeer的9年科学出版物数据集上的应用结果,显示了改进的语义内聚性主题检测和捕获了作者对主题演变的兴趣的转移。我们还讨论了模型在新挖掘和推荐场景中的使用机会。
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: 社会化媒体; 时空动态分析; 信息检索
课程来源: 视频讲座网
最后编审: 2019-10-31:lxf
阅读次数: 46