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10/25/09L. Baltrunas & X. Amatriain
Towards Time-Dependant Recommendation based on Implicit Feedback
Linas Baltrunas and Xavier Amatriain
10/25/09L. Baltrunas & X. Amatriain
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
10/25/09L. Baltrunas & X. Amatriain
Outline
Approach & Challenges
Last.fm data set
Evaluation protocol
Empirical study
Latest and future work
10/25/09L. Baltrunas & X. Amatriain
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.
10/25/09L. Baltrunas & X. Amatriain
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]
10/25/09L. Baltrunas & X. Amatriain
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.
10/25/09L. Baltrunas & X. Amatriain
Error Measure: Our Approach
We allow only non overlapping partitioning
We propose to compute error E, given partitioning, recommender and data:
10/25/09L. Baltrunas & X. Amatriain
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.
10/25/09L. Baltrunas & X. Amatriain
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.
10/25/09
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:
Cross Validation – expensive, accuracy can be increased by adding more folds.
Explained Variance.
Information Gain.
True Error Cross Validation
Explained Variance Information Gain
10/25/09L. Baltrunas & X. Amatriain
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
10/25/09L. Baltrunas & X. Amatriain
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
10/25/09L. Baltrunas & X. Amatriain
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.
10/25/09L. Baltrunas & X. Amatriain
Questions? Answers? Ideas?