The Journal of Machine Learning Research
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 17th international conference on World Wide Web
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Data leakage mitigation for discretionary access control in collaboration clouds
Proceedings of the 16th ACM symposium on Access control models and technologies
Enriching employee ontology for enterprises with knowledge discovery from social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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People are increasingly using more and more social softwares, generating flooding communications. User analytics may be performed to mine a person's activities on different social systems and extract patterns, be it interest patterns, social patterns, or work patterns. Such patterns may benefit both the individuals and the organizations the users associated with, as the information is valuable in numerous tasks, including recommendation, evaluation, management, and so on. In this article, we present an actionable solution of user analytics, namely collaboration analytics, by focusing on mining a person's work patterns from her collaboration activities. Our solution effectively makes use of a user's heterogeneous data collected from various collaboration tools to derive an integrated description of the user's collaborative work. A number of ``work areas'', each of which contains its work topics and people involved, are generated for every user. The challenges we face include the clustering of items with short texts and prioritizing/weighting data items based on importance/relevance. Our solutions to those issues will be described in this article. In particular, we mine users' background information from various types of data and use such information to enrich the semantics of the short texts contained in the activity instances on collaboration tools before clustering those instances into work areas. Finally, we have developed a prototype of our collaboration analytics solution and evaluated it with real-world data and people.