Time-dependand Recommendation based on Implicit Feedback

10/25/09L. Baltrunas & X. Amatriain

Towards Time-Dependant Recommendation based on Implicit Feedback

Linas Baltrunas and Xavier 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


Outline

Approach & Challenges

Last.fm data set

Evaluation protocol

Empirical study

Latest and future work


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.


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]


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.


Error Measure: Our Approach

We allow only non overlapping partitioning

We propose to compute error E, given partitioning, recommender and data:


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.


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


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


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


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.


Questions? Answers? Ideas?

Time-dependand Recommendation based on Implicit Feedback

Technology

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