Towards Time-Dependant
Recommendation based on
Implicit Feedback

Linas Baltrunas and Xavier Amatriain


         L. Baltr...
Goal
   Long-term goal is to design a time-aware
    recommender system, which can accurately
    predict user's taste, g...
Outline
   Approach & Challenges
 Last.fm   data set
 Evaluation   protocol
 Empirical   study
 Latest   and future w...
Approach: Challenges

 Approach

    How to combine the predictions generated for
     each of the profiles and how to p...
Last.fm Data
   Implicit data:
       Collected during a two year
        period
     Only   Spanish users
     #users...
Evaluation of the System
   The evaluation of a recommender system tries to
    estimate the users' satisfaction for a gi...
Error Measure: Our Approach

   We allow only non
    overlapping partitioning




                                      ...
Experimental Evaluation
   We used Last.fm data.
   Matrix factorization as
    the rating prediction
    method.
 We  ...
Accuracy of the Method
   We use a pre-defined time segmentation, for day,
    week and year.
 When    using only the da...
Towards Optimal Split of the Profiles
                                            Day cycle is partitioned
              ...
Current work (1)
   Generating artificial profiles
     ●   In order to evaluate the goodness of the segmentation
       ...
Current work (2)
 Is   the approach domain or dataset specific?
       ●   We are currently working on using the same
   ...
Future Work
 Finding
        Optimized Segments Including variable number
 and per-user segmentation
 Evaluation    of t...
Questions? Answers? Ideas?




           L. Baltrunas & X. Amatriain   10/25/09
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Time-dependand Recommendation based on Implicit Feedback

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Presentation given at the Context-aware Recommendation workshop at #recsys09

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Time-dependand Recommendation based on Implicit Feedback

  1. 1. Towards Time-Dependant Recommendation based on Implicit Feedback Linas Baltrunas and Xavier Amatriain L. Baltrunas & X. Amatriain 10/25/09
  2. 2. Goal  Long-term goal is to design a time-aware recommender system, which can accurately predict user's taste, given the current time. ● The vision is to model a single user u by many micro profiles u1, u2, ..., un that best represent the user in a particular time span.  Challenges  Implicit user feedback  Continuous temporal domain  Predict taste on new items rather than user behavior L. Baltrunas & X. Amatriain 10/25/09
  3. 3. Outline  Approach & Challenges  Last.fm data set  Evaluation protocol  Empirical study  Latest and future work L. Baltrunas & X. Amatriain 10/25/09
  4. 4. Approach: Challenges  Approach  How to combine the predictions generated for each of the profiles and how to present the final predictions.  Future work  How to discover meaningful time partitions (micro- profile) based on the time cycles. Each partition should represent a time slice where user has similar repetitive behavior.  Investigated a simple non-personalized, non- overlapping case of time partitioning. L. Baltrunas & X. Amatriain 10/25/09
  5. 5. Last.fm Data  Implicit data:  Collected during a two year period  Only Spanish users  #users 338  #tracks 322.871  #artists 16.904  #entries 1.970.029  We converted it to explicit data: 1 to 5 stars system [Celma'08] L. Baltrunas & X. Amatriain 10/25/09
  6. 6. Evaluation of the System  The evaluation of a recommender system tries to estimate the users' satisfaction for a given recommendation.  Our goal is to predict the taste on new items rather than user behavior.  We measure the accuracy of the system using Mean Absolute Error (MAE).  Problem with continuous contextual variable:  The exact partitioning of the time domain defines the ground truth that we want to predict. L. Baltrunas & X. Amatriain 10/25/09
  7. 7. Error Measure: Our Approach  We allow only non overlapping partitioning  We propose to compute error E, given partitioning, recommender and data: L. Baltrunas & X. Amatriain 10/25/09
  8. 8. Experimental Evaluation  We used Last.fm data.  Matrix factorization as the rating prediction method.  We used 5 fold cross- validation.  Finally, we do not look into personalized partitions but rather evaluate global ones. L. Baltrunas & X. Amatriain 10/25/09
  9. 9. Accuracy of the Method  We use a pre-defined time segmentation, for day, week and year.  When using only the data of the segment the accuracy E of the prediction improved for all our observed segmentations. L. Baltrunas & X. Amatriain 10/25/09
  10. 10. Towards Optimal Split of the Profiles  Day cycle is partitioned into two segments each spanning for 12 hours.  We used 3 different methods to predict the best partitioning: True Error Cross Validation  Cross Validation – expensive, accuracy can be increased by adding more folds.  Explained Variance.  Information Gain. Explained Variance Information Gain   10/25/09
  11. 11. Current work (1)  Generating artificial profiles ● In order to evaluate the goodness of the segmentation measures we need a ground truth ● We inject artificial temporal changes in user profiles and then compute how well the different segmentation measures detect them L. Baltrunas & X. Amatriain 10/25/09
  12. 12. Current work (2)  Is the approach domain or dataset specific? ● We are currently working on using the same approach on IPTV data using viewing data ● Initial results are promising but not conclusive L. Baltrunas & X. Amatriain 10/25/09
  13. 13. Future Work  Finding Optimized Segments Including variable number and per-user segmentation  Evaluation of the micro-profiling approach:  Prediction generation using (hierarchical) micro-profiles at different temporal granularity  Recommendations at different levels, i.e., genre, artist, album and track.  Extend the context information to include:  The current song.  The current album.  The current genre and mood of a song. L. Baltrunas & X. Amatriain 10/25/09
  14. 14. Questions? Answers? Ideas? L. Baltrunas & X. Amatriain 10/25/09
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