诺基亚北京研究院将参加在今年10月26日至10月31日举行的第十九届CIKM（The ACM Conference of Information Retrieval and Knowledge Management）大会。CIKM是国际知名的信息检索和知识管理领域的学术会议。本届大会将在加拿大多伦多召开。会议收录了两篇诺基亚研究中心提交的关于情景数据挖掘和管理的学术论文，论文的简要信息如下：

题目：An Effective Approach for Mining Mobile User Habits

作者：Huanhuan Cao, Tengfei Bao, Qiang Yang, Enhong Chen, Jilei Tian

简介: 研究用户和移动设备的交互行为对于用户行为理解有着重要的意义。在这篇论文中，我们提出在用户情景数据日志中挖掘关于用户交互行为和情景模式的关联规则来刻画用户习惯。在文章中，我们把这种关联规则称为用户行为模式。基于真实数据集的实验清楚的表明这种方法能够有效的挖掘用户习惯。

Abstract: The user interaction with the mobile devices plays an important role in user habit understanding. In this case, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.

 题目：Topic Detection and Organization of Mobile Text Messages

作者: Ye Tian, Jinghai Rao, Wendong Wang, Xueli Wang, Canfeng Chen and Jian Ma

简介: 在文章中，我们介绍了一个在手机上运行的短信聚类系统。该系统可以将用户收发的短信按照话题组织在一起，并提供简单的总结。针对短信文字较短，从单个短信中很难提取出语义信息的特点，我们首先使用短信的收发时间进行预分类，再根据LDA语义模型将短信按话题组织成对话形式。我们使用50个大学生志愿者在6个月收发的122，359条短信来评估该系统，证明准确率高达90%以上。

Abstract: How to organize and visualize big amount of text messages stored on one’s mobile phone is a challenging problem, since they can hardly be organized by threads as we do for emails due to lack of necessary metadata such as “subject” and “reply-to”. In this paper, we propose an innovative approach based on clustering algorithms and natural language processing methods. We first cluster the text messages into candidate conversations based on their temporal attributes, and then do further analysis using a semantic model based on Latent Dirichlet Allocation (LDA). Considering that the text messages are usually short and sparse, we trained the model using a large scale external data collected from twitter-like web sites, and applied the model to text messages. In the end, the text messages are organized as conversations based on their topics. We evaluated our approach based on 122,359 text messages collected from 50 university students during 6 months.