Towards Time-Dependant
Recommendation based on
Implicit Feedback
Linas Baltrunas and Xavier Amatriain
L. Baltrunas & X. Amatriain 10/25/09

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

Outline
 Approach & Challenges
 Last.fm data set
 Evaluation protocol
 Empirical study
 Latest and future work
L. Baltrunas & X. Amatriain 10/25/09

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

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

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

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

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

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

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

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

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

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

Questions? Answers? Ideas?
L. Baltrunas & X. Amatriain 10/25/09