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Learning user preferences for 2CP-regression for a recommender system

Learning user preferences for 2CP-regression for a recommender system. Alan Eckhardt, Peter Vojtáš Department of Software Engineering, Charles University in Prague, Czech Republic. Outline. Motivation User model Peak and 2CP Experiments Conclusion and future work.

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Learning user preferences for 2CP-regression for a recommender system

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  1. Learning user preferences for 2CP-regression for a recommender system Alan Eckhardt, Peter Vojtáš Department of Software Engineering, Charles University in Prague, Czech Republic

  2. Outline • Motivation • User model • Peak and 2CP • Experiments • Conclusion and future work SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  3. User preference learning • Helping the user to find what she looks for • E.g. notebooks • A small amount of information required from the user • Ratings of notebooks,... • Construction of a general user preference model • Each user has his/her own preference model • Recommendation of the top k notebooks to the user • Which the preference model has chosen as the most preferred for the user SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  4. User preference learning • Recommendation process • Initial set • Centers of clusters of objects • Construction of user model • Recommendation • More iterations possible • In each iteration the user model is refined SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  5. Two step user model • User model learning is divided into two steps • Local preferences - normalization of the attribute values of notebooks to their preference degrees Transforms the space into [0,1]N • Global preferences - aggregation of preference degrees of attribute values into the predicted rating SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  6. User model • Fuzzy sets • Normalize the space to monotone space [0,1]N • Define pareto front • Set of incomparable objects • Candidates for the best object • (1,…,1) is the best object 1 1 0 SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  7. User model • Aggregation • Resolves the best object from pareto front • The second best object may not be on pareto front • Two methods – Statistical and Instances 1 1st best 2nd best 1 SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010 0

  8. Normalization of numerical attributes • Linear regression • Preference of the smallest or the largest value • Quadratic regression • Can detect ideal values, but often fails in experiments SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  9. 2CP regression Preference dependence between attributes This is not a dependence in the dataset (e.g. the resolution of display influences the price) The influence of the value of attribute A1 on the preference of attribute A2 E.g. the value of the producer (IBM) of a notebook influence the preference of the price of the notebook (for IBM, the ideal price is 2200$). SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  10. Peak • Motivation • User often prefer once particular value of attribute • Finding the peak value • Traversing the training set • Which is small • Testing the error of linear regressions on both sides of the peak • We know exactly which value is the most preferred • Useful for visual representation SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  11. 2CP regression+Peak Dependence of price on the value of manufacturer ACER => High price ASUS => Lower price SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  12. Experiment settings • Dataset of 200 notebooks • Artificial user preferences • The preference of price was dependent on the value of producer • Training sets of sizes 2-60 • The rest of the dataset was used as testing set • Error measures • RMSE • Kendall t coefficient SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  13. Experiment settings • Tested methods • Support Vector Machines from Weka • Mean – returns the average rating from the training set • Instances – classification, uses objects from the training as boundaries on rating • Statistical – weighted average with learned weights • 2CP • Both Instances and Statistical can use local preference normalization – Linear, Quadratic, Peak • 2CP serves to find the relation between the preference of an attribute value and the value of another SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  14. Experiment results SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  15. Experiment results SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  16. Experiment results SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  17. Conclusion • Proposal of method Peak • Combination with 2CP • Experimental evaluation with very good results • Using rank correlation measure SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

  18. Future work • nCP-regression • Clustering of similar values for better robustness • Degree of relation between two attributes SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

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