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Dataset-driven research to improve TEL recommender systems
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Dataset-driven research to improve TEL recommender systems

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  • 1. Dataset-driven research to improve TEL recommender systems
    Katrien Verbert, HendrikDrachsler,
    Nikos Manouselis, Martin Wolpers, RiinaVuorikari and Erik Duval
  • 2. What is dataTEL?
    dataTEL is a Theme Team funded by the STELLAR network of excellence.
    It addresses 2 STELLAR Grand Challenges
    Connecting Learner
    Contextualization
  • 3. dataTEL::Objective
    Five core questions:
    How can data sets be shared according to privacy and legal protection rights?
    How to develop a respective policy to use and share data sets?
    How to pre-process data sets to make them suitable for other researchers?
    How to define common evaluation criteria for TEL recommender systems?
    How to develop overview methods to monitor the performance of TEL recommender systems on data sets?
    Standardize research on recommender systems in TEL
  • 4. Free
    the data
    B Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
  • 5. Why?
    By Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
  • 6. Because we
    will get new
    insights
    By Tom Raftery http://www.flickr.com/photos/traftery/4773457853/sizes/l
  • 7.
  • 8. dataTEL challenge & dataTEL cafe event
    a call for TEL datasets
    eight datasets submitted
    http://bit.ly/ieqmWW
  • 9. http://dev.mendeley.com/datachallenge/
  • 10.
  • 11. Collaborative filtering
    Users who bought the same product also bought product B and C
  • 12. User-based CF
    A
    Sam
    high correlation
    B
    Ian
    C
    Neil
  • 13. Item-based CF
    A
    Sam
    B
    high correlation
    Ian
    C
    Neil
  • 14. similarity measures
    Cosine similarity
    Pearson correlation
    Tanimoto or extended Jaccard coefficient
  • 15. evaluation metrics
    Accuracy: precision, recall, F1
    Predictive accuracy: MAE, RMSE
    Coverage
  • 16. experiments
    Collaborative filtering based on ratings
    Collaborative filtering based on implicit relevance data
  • 17. similarity measures
    MAE of item-based collaborative filtering based on different similarity metrics
  • 18. algorithms
    MAE of user-based, item-based and slope-one collaborative filtering
    Nikos Manouselis, Riina Vuorikari, and Frans Van Assche. Simulated analysis of
    MAUT collaborative filtering for learning object recommendation (SIRTEL07)
  • 19. implicit relevance data
    F1 of user-based collaborative filtering with increasing number of neighbors
  • 20. data dimensions
  • 21. CEN WS-LT Social Data
    standardized representation of both explicit and implicit relevance data
    http://bit.ly/ho1MbC
  • 22. Data set framework to monitor performance
    22
  • 23. evaluation criteria
    1. Reaction of learner
    2. Learning improved
    3. Behaviour
    4. Results
    1. Accuracy
    2. Coverage
    3. Precision
    1. Effectiveness of learning
    2. Efficiency of learning
    3. Drop out rate
    4. Satisfaction
    Kirkpatrick model by Manouselis et al. 2010
    Combine approach by Drachsler et al. 2008
  • 24. So what about you…
    Do you have data that can be shared for research?
    Do you want to be involved in dataTEL research?
    datafortel@gmail.com
  • 25. dataTEL challenge at I-KNOW 2011
    11th International Conf. on Knowledge Management and Knowledge Technologies
    7–9 September 2011, Messe Congress Graz, Austria
  • 26. Many thanks for your attention!
    Slides are available at: http://www.slideshare.net/kverbert
    Email: katrien.verbert@cs.kuleuven.be
    Skype: katrien.verbert
    Twitter: katrien_v