Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Sharing Expertise: Beyond Knowledge Management
Sharing Expertise: Beyond Knowledge Management
A Distributed Genetic Algorithm for Employee Staffing and Scheduling Problems
Proceedings of the 5th International Conference on Genetic Algorithms
Graph-based ranking algorithms for e-mail expertise analysis
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
IBM Journal of Research and Development - Business optimization
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Bluereach: harnessing synchronous chat to support expertise sharing in a large organization
CHI '08 Extended Abstracts on Human Factors in Computing Systems
PROSPECT: a system for screening candidates for recruitment
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient multifaceted screening of job applicants
Proceedings of the 16th International Conference on Extending Database Technology
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Effective management of human resources is a significant challenge faced by most organizations. In this paper, we look at two problems that arise in large, globally distributed organizations: staffing projects with the required subject matter experts and connecting subject matter experts to other employees who can benefit from their expertise. Several approaches based on automated skill matching have been suggested in the past to solve these problems. However, we argue that social relationships play an important role in both of these functions, and better matches can be obtained by combining skill matching with rich social interaction data. We describe two systems that exploit social networking data to solve these problems and report the results of real life experiments performed using these systems.