Call for paper 〔OPEN〕

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Registration 〔OPEN〕

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〔CLOSED〕
Introduction
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.
Call for paper

Important date

2014-07-28
Draft paper submission deadline

Submission Topics

We invite the submission of papers reporting original research, studies, advances, experiences, or work in progress in the scope of recommender system utility evaluation. The topics the workshop seeks to address include–though need not be limited to–the following: Recommendation quality dimensions Effective accuracy, ranking quality Novelty, diversity, unexpectedness, serendipity Utility, gain, cost, risk, benefit Robustness, confidence, coverage, ease of use, persuasiveness, etc. Matching metrics to tasks, needs, and goals User satisfaction, user perception, human factors Business-oriented evaluation Multiple objective optimization, user engagement Quality of service, quality of experience Evaluation methodology and experimental design Definition and evaluation of new metrics, studies of existing ones Adaptation of methodologies from related fields: IR, Machine Learning, HCI, etc. Evaluation theory Practical aspects of evaluation Offline and online experimental approaches Simulation-based evaluation Datasets and benchmarks Validation of metrics Efficiency and scalability Open evaluation platforms and infrastructures
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Important Date
  • Oct 10

    2014

    Conference Date

  • Jul 28 2014

    Draft paper submission deadline

  • Oct 10 2014

    Registration deadline

Sponsored By
美国计算机学会