Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Model-Based Diagnosis or Reasoning from First Principles
IEEE Intelligent Systems
A Recommendation System for Software Function Discovery
APSEC '02 Proceedings of the Ninth Asia-Pacific Software Engineering Conference
Software Estimation: Demystifying the Black Art
Software Estimation: Demystifying the Black Art
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
A collaborative constraint-based meta-level recommender
Proceedings of the 2008 ACM conference on Recommender systems
Not all classes are created equal: toward a recommendation system for focusing testing
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
Potentials and challenges of recommendation systems for software development
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
Dimensions of tools for detecting software conflicts
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
The IT Measurement Compendium: Estimating and Benchmarking Success with Functional Size Measurement
The IT Measurement Compendium: Estimating and Benchmarking Success with Functional Size Measurement
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Estimating a project's effort or schedule is a crucial task for software project management. However, project leaders are often overwhelmed when selecting an appropriate estimation method to best match the project characteristics and context. Recommender systems (RS) are applications that typically support online users when confronted with large sets of choices. Knowledge-based recommenders are a specific variant of these systems that exploit explicit knowledge models in order to infer matching items based on a set of specific requirements. This paper's contribution lies in its application of knowledge-based recommendation mechanisms to the domain of software project management and presents a recommender for effort estimation methods. An initial evaluation among software professionals showed promising results and disclosed helpful hints for further development.