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Dataset-driven research to improve TEL recommender systems


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Dataset-driven research to improve TEL recommender systems

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