Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases
ACM Transactions on Database Systems (TODS)
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Concept decompositions for large sparse text data using clustering
Machine Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
A consensus based approach to constrained clustering of software requirements
Proceedings of the 17th ACM conference on Information and knowledge management
A recommender system for requirements elicitation in large-scale software projects
Proceedings of the 2009 ACM symposium on Applied Computing
Automated support for managing feature requests in open forums
Communications of the ACM - A View of Parallel Computing
A recommender system for dynamically evolving online forums
Proceedings of the third ACM conference on Recommender systems
Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation
RE '09 Proceedings of the 2009 17th IEEE International Requirements Engineering Conference, RE
Collaborative filtering recommender systems
The adaptive web
A Machine Learning Approach for Identifying Expert Stakeholders
MARK '09 Proceedings of the 2009 Second International Workshop on Managing Requirements Knowledge
Towards a Research Agenda for Recommendation Systems in Requirements Engineering
MARK '09 Proceedings of the 2009 Second International Workshop on Managing Requirements Knowledge
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Requirements Engineering involves a number of human intensive activities designed to help project stakeholders discover, analyze, and specify the functional and non-functional needs for a software intensive system. Recommender systems can support several different areas of this process including identifying potential subject matter experts for a topic, keeping individual stakeholders informed of relevant issues, and even recommending possible features for stakeholders to consider and explore. This position paper summarizes an extensive series of experiments that were conducted to identify best-of-breed algorithms for recommending forums to stakeholders and recommending unexplored topics to project managers.