Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
The Conference Assistant: Combining Context-Awareness with Wearable Computing
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
Information diffusion through blogspace
ACM SIGKDD Explorations Newsletter
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Integrating multiple contexts in real-time collaboration applications
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
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The amount of information exchanged in the communication increases rapidly as the user plays a more role in her organization and social networks. It has become overwhelming to many people in this communication and information centric era and the need for assistance to manage communicated information and the communication relations has grown. We present a communication data based user activity recommender system that aims to help the user use the communication services more effectively and easily. The communication data of a user provides abundant information about the topics the user is working on, the people the user communicates with, and the communication and information needs of the user. Our system analyzes the communication data and extracts such information, generates recommendations for user's communication services, and provides the information the user needs according to the user's communication context. Machine learning and natural language processing methods are utilized for communication data analysis and the performance of an example of our recommenders -- predictive meeting assistant -- is presented in details. Our recommender system has been implemented for large-scale deployments and its core architecture is presented.