OrdRec : An Ordinal Model to Predict User Rating Distributions

1. OrdRec:AnOrdinal Model to Predict User Rating Distributions Yehuda Koren , Joe Sill RecSys’11 Best Paper Award

2. Motivation • Humans are good at comparing, not rating • Implicit feedback: click, star, shopping cart… • Solution: • Numerical? People may have different standards… • Categorical? 5 stars is more similar to 4, not 1

3. Motivation • Think of rating as random variable • With user bias, we can predict mean • What about a full rating distribution? • More confidence on recommendation • Choose conservative/adventurous rec’ style

4. SVD++: MF with Implicit Feedback • Again users are mapped into space with dim • Users are also characterized by what they rated (and what they did not) • So items are mapped into two vectors in • We use SVD++ predicted rating as mean

5. Two phase scheme • Derive user specific rating cutting points • Rank items for user , using cutting points

6. Cutting Point Determination • Mean: predicted rating from SVD++ • Distribution: assumed normal/logistic • Derive probability of observed ratings under • Maximize likelihood

7. Rank Items with Cutting Points • With user specific cutting points • We need to differentiate preference on… • Item 1 with probability ¾ rated A, ¼ rated F • Item 2 with probability 1 rated C • Model user preference • Linear regression on ordered item pairs

8. Evaluation and Results

9. Results

10. Result Analysis • OrdRec as leader on Nexflix for both RMSE and FCP • Better model ordinal semantics of user ratings • SVD++ performs best in terms of RMSE • The only methods trained to minimize RMSE • RMSE values on Y!Music much greater than Netflix while FCP values changes little • RMSE more sensitive to rating scales than FCP (Y!Music 10 scales, Netflix 4 scales)

11. Thank you for listening! Q&A