Yahoo! Music Recommendations

1. Yahoo! Music Recommendations Modeling Music Ratingswith Temporal Dynamics and Item Taxonomy Yahoo! Research

2. Outline • Features of Yahoo! Music Dataset • Basic Latent Factor Model • Improved modelling with • Taxonomy • Temporal Dynamics • Experiments and Result

3. Features of Yahoo! Music Dataset • 1st track of KDDCUP 2011 • Ratings over nearly 10 years • Items: 624,96 • Users: 1,000,990 • Ratings: 262,810,175 • Ratings were given to 4 different types • Track (item to be recommended) • Artist, Album, and Genre

4. Basic Latent Factor Model • Regularized SVD • Each user and item in same latent factor space • Rating as cosine similarity (dot product) • User and item become singular vectors • Stochastic Gradient Descent • Go through each rating and iterates • Good performance with sparse rating matrix

5. Incorporate Dataset Features • Two dimension • Bias • Personalization • Two effects • Taxonomy • Temporal effect • User session • Long termdynamics

6. Bias Modelling • Why bias, and basic bias model • Taxonomy biases • Item based • User based • Temporal effect • User session • Long term

7. Why Bias Modelling? • Lack of personalization • Not saying they are of no importance! • Netflix Prize Data • 52.9% of observed variance is explained • 41.4% by user and item bias • 11.5% by personalization • Separate changing effects from those unchanged

8. Bias with Taxonomy • Observation • Item biases share components by taxonomy! • Item bias • Album • Artist • Genre • User bias • Personal taste of a particular type affect all songs of this type

9. Bias with Temporal Effects • User session • Drifting effect: context of ratings • Human are more capable on comparing rather than rating on absolute scale: more on that later • History session bias is discarded as noise • Current session bias is retained for prediction

10. Bias with Temporal Effects • Long term dynamics of items • New songs’ ratings have different patterns compared to old songs • Steady after 360 weeks

11. Personalization • Apply the same techniques to personalization • Taxonomy for items • Model album/artist effect independently • Genre did not improve result • Session specific factors for users • Sudden change of user’s taste • History session factors are discarded as noise • Keep current session factor for prediction

12. Learning the Model • What parameters to learn? • Two Phase Learning • Bias, User, Item…Phase 1 • Session specific parameters…Phase 2 • Stochastic Gradient Descent • Go through each rating available • L2 regularized • Cyclic iteration: sweep forward/backward alternatively to avoid discontinuity across iterations

13. Evaluation and Results

14. Result Analysis • Bias takes ~80% of explained variance • Taxonomy: reduces 0.6 • User session: reduces 0.9 • Long term: reduces 0.1 • Latent factors did not improve much • Taxonomy: reduces 0.2 • User session: reduces 0.2

15. Result Analysis • Released prior to KDDCUP 2011 • Best score by NTU on 1st track: • Single model: 22.90 (lower than this model, 22.59) • Ensemble: 21.01 (blending/stacking is key)