CfP: Rule-based Recommender Systems Challenge at

March 16, 2015 at 11:18 AM
The challenge has two focus areas: (1) rule learning algorithms applied on recommender problems and (2) using the linked open data cloud for feature set extension. It uses a semantically enriched version of the MovieLens dataset. In addition to the standard metrics, the challenge aims to assess the understandability of the rule set generated by the participating rule-based systems.
A challenge of, August 2-5, 2015 Berlin
Important dates
  • Paper and result submission: May 23, 2015
  • Author Notification: June 6, 2015
  • Challenge: 3-5 Aug, 2015
The Task
The participating systems are requested to find and recommend a limited set of 5 items that best match a user profile.
  • The participants will be provided with a semantically enriched version of the MovieLens dataset.
  • It is mandatory that a participating solution either uses the linked open data cloud to further extend the feature set or is a rule-based classifier. Both options simultaneously are preferred.
  • A scorer is provided by the organizers so that the participants can check their progress.
  • Challenge submission will consist of the set of additional recommendations (top-5 movies) for each user from the train dataset and a file containing the rules that lead to the prediction (rule based classifiers only, PMML RuleSet model preferred but not required).
Judging and Prizes
Four prizes, totalling to 500 USD, will be given:
  • best recommender performance will be given to the paper with the highest score in the evaluation (main prize)
  • most understandable ruleset (only rule-based submissions eligible)
  • best aggregate diversity
  • most original approach, selected by the Challenge Program Committee with the reviewing process

Rule Challenge 2015 proceedings will be published as CEUR Proceedings and indexed by SCOPUS.

  • Jaroslav Kuchař (Czech Technical University, Prague)
  • Tommaso di Noia (Politecnico di Bari, Italy)
  • Heiko Paulheim (University Mannheim, Germany)
  • Tomáš Kliegr (University of Economics, Prague)
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