Evaluation is a cardinal issue in recommender systems; as in almost any other technical discipline, it highlights to a large extent the problems that need to be solved by the field and, hence, leads the way for algorithmic research and development in the community. Yet, in the field of recommender systems, there still exists considerable disparity in evaluation methods, metrics, and experimental designs as well as a significant mismatch between evaluation methods in the lab and what constitutes an effective recommendation for real users and businesses. This workshop aims at providing an informal forum to tackle such issues and move towards better understood and commonly agreed evaluation methodologies, allowing one to leverage the efforts and the workforce of the academic community on meaningful and relevant directions to real-world developments. REDD 2014 aims at gathering researchers and practitioners interested in better understanding the unmet needs of the field in terms of evaluation methodologies and experimental practices. The main goal of this workshop is to provide an informal setting for discussing and exchanging ideas, experiences, and viewpoints. REDD seeks to identify and better understand the current gaps in recommender system evaluation methodologies, help lay directions for progress in addressing them, and foster the consolidation and convergence of experimental methods and practices.