RepSys 2013 - International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys)

This workshop aims to gather researchers and practitioners interested in defining clear guidelines for their experimental needs to allow fair comparisons to related work. The workshop will provide an informal setting for exchanging and discussing ideas, sharing experiences and viewpoints. We seek to identify and better understand the current gaps in the implementation of recommender system evaluation methodologies, help lay directions for progress in addressing them, and foster the consolidation and convergence of experimental methods and practice. As a particular focus of interest, the workshop aims to discover which are the main challenges related to reproduction and replication of prior research, along with an exploration of possible directions to overcome these limitations.
Specific questions that the workshop aims to address include the following:
How important is the reproducibility and replication of experiments for the community?
What are the challenges for replication of evaluation in the RS field? How could we facilitate easier and more accurate comparison with prior work?
How can methods and metrics be more clearly and/or formally defined within specific tasks and contexts for which a recommender application is deployed?
What parts -if any- of an online experiment could be reproducible (and how)?
How should the academic evaluation methodologies be described to improve their relevance, usefulness, and replicability for industrial settings?
What type of public resources (data sets, benchmarks) should be available, and how can they be built? Is it possible to have a generic framework for the evaluation (and replication) of recommender systems?
To what extent is it possible to reuse experimental methodologies across domains and/or businesses?
How do we envision the evaluation of recommender systems in the future and how does this affect the replicability of said systems?
Scope and topics
Papers explicitly dealing with replication of previously published experimental conditions/algorithms/metrics and the resulting analysis are encouraged. In particular, we seek discussions on the difficulties the authors may find in this process, along with their limitations or successes on reproducing the original results.
Within the broader scope of recommender system evaluation, the presented papers and discussions to be held at the workshop will address ?though need not be limited to? the following topics:
Limitations and challenges of experimental reproducibility and replication
Reproducible experimental design
Replicability of algorithms
Standardization of metrics: definition and computation protocols
Evaluation software: frameworks, utilities, services
Reproducibility in user-centric studies
Datasets and benchmarks
Recommender software reuse
Replication of already published work
Reproducibility within and across domains and organizations
Reproduction and replication guidelines