Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases
ACM Transactions on Database Systems (TODS)
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
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
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Concept decompositions for large sparse text data using clustering
Machine Learning
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A unified framework for model-based clustering
The Journal of Machine Learning Research
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
RE '08 Proceedings of the 2008 16th IEEE International Requirements Engineering Conference
Collaborative filtering recommender systems
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Automated support for managing feature requests in open forums
Communications of the ACM - A View of Parallel Computing
Lessons Learned from Open Source Projects for Facilitating Online Requirements Processes
REFSQ '09 Proceedings of the 15th International Working Conference on Requirements Engineering: Foundation for Software Quality
A recommender system for dynamically evolving online forums
Proceedings of the third ACM conference on Recommender systems
Information and Software Technology
Utilizing recommender systems to support software requirements elicitation
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
On-demand feature recommendations derived from mining public product descriptions
Proceedings of the 33rd International Conference on Software Engineering
Speculative analysis of integrated development environment recommendations
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Mining textual requirements to assist architectural software design: a state of the art review
Artificial Intelligence Review
The state of the art in automated requirements elicitation
Information and Software Technology
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In large and complex software projects, the knowledge needed to elicit requirements and specify the functional and behavioral properties can be dispersed across many thousands of stakeholders. Unfortunately traditional requirements engineering techniques, which were primarily designed to support face-to-face meetings, do not scale well to handle the needs of larger projects. We therefore propose a semi-automated requirements elicitation framework which uses data-mining techniques and recommender system technologies to facilitate stakeholder collaboration in a large-scale, distributed project. Our proposed recommender model is a hybrid one designed to manage the placement of stakeholders into highly focused discussion forums, where they can work collaboratively to generate requirements. In our approach, statements of need are first gathered from the project stakeholders; unsupervised clustering techniques are then used to identify cohesive and finely-grained themes and a users' profile is constructed according to the interests of the stakeholders in each of these themes. This profile feeds information to a collaborative recommender, which predicts stakeholders' interests in additional forums. The validity and effectiveness of the proposed recommendation framework is evaluated through a series of experiments using feature requests from three software systems.