1. The Wisdom of the Few A Collaborative Filtering Approach Based on Expert Opinions from the Web Xavier Amatriain Telefonica Research Nuria Oliver Telefonica Research Josep M. Pujol Telefonica Research Neal Lathia Dept. of Computer Science University College of London Haewoon Kwak Computer Science Dept. KAIST SIGIR 2009

2. Standard Collaborative Filtering Collaborative filtering is the preferred approach for Recommender Systems Recommendations are drawn from your past behavior and that of similar users in the system Standard CF approach: Find your Neighbors from the set of other users Recommend things that your Neighbors liked and you have not “seen” Problem: predictions are based on a large dataset that is sparse and noisy

3. Expert-based Collaborative Filtering Expert : individual that we can trust to have produced thoughtful, consistent and reliable evaluations (ratings) of items in a given domain Find neighbors from a reduced set of experts instead of regular users. 1. Identify domain experts with reliable ratings 2. For each user, compute “expert neighbors” 3. Compute recommendations similar to standard kNN CF

4. User Data Collection Netflix data set 10,000 users 17,770 movies

5. Experts Data Collection Collections of expert ratings can be obtained almost directly on the web: we crawled the Rotten Tomatoes movie critics mash-up 8,000 movies overlap with Netflix Only those (169) with more than 250 ratings in the Neflix dataset were used

6. Dataset Analysis (# ratings) Sparsity coefficient: 0.01 (users) vs. 0.07 (experts) Average movie has 1K user ratings vs. 100 expert ratings Average expert rated 400 movies, 10% rated > 1K

7. Dataset Analysis (average rating) Users: average movie rating ~0.55 (3.2 ⋆ ); 10%  0.45(2.8 ⋆ ),10%  0.7(3.8 ⋆ ) Experts: average movie rating ~0.6 (3.4 ⋆ ) 10%  0.4(2.6 ⋆ ), 10%  0.8 (4.2 ⋆ ) user ratings centered 0.7 (3.8 ⋆ ) expert ratings centered 0.6 (3.4 ⋆ ): small variability only 10% of the experts have a mean score  0.55 (3.2 ⋆ ) and another 10%  0.7 (3.8 ⋆ )

8. Dataset Analysis (std) Users: per movie centered around 0.25, little variation per user centered around 0.25, larger variability Experts: lower std per movie (0.15) and larger variation. average std per expert = 0.2, small variability.

9. Dataset Analysis Summary Experts: Are much less sparse Rate movies all over the rating scale instead of being biased towards rating only “good” movies (different incentives). Have a lower overall standard deviation per movie: they tend to agree more than regular users. Tend to deviate less from their personal average rating.

10. Method {e 1,e 2,…,e n }

11. Evaluation Procedure Use the 169 experts to predict ratings from 10,000 users sampled from the Netflix dataset Prediction MAE(Mean Absolute Error), 5-fold cross- validation Top-N precision by classifying items as being “recommendable” given a threshold User Study

12. Role of Thresholds MAE is inversely proportional to the similarity threshold (  ) until the 0.06 mark, when it starts to increase as we move to higher  values. below 0.0 it degrades rapidly: too many experts; Coverage decreases as we increase . For the optimal MAE point of 0.06, coverage is still above 70%. MAE as a function of the confidence threshold (  )  =0.0 and  =0.01(optimal around 9)

13. Comparison to Standard CF Expert-CF only works worse for the 10% of the users with lower MAE

14. Top-N Precision Precision of the Top-N Recommendations as a function of the “recommendable” threshold For a threshold of 4, NN-CF outperforms expert-based but if we lower it to 3 they are almost equal

15. User Study 57 participants, only 14.5 ratings/participant 50% of the users consider Expert-based CF to be good or very good Expert-based CF: only algorithm with an average rating over 3 (on a 0-4 scale)

16. User Study Results to the questions: “The recommendation list includes movies I like/dislike” (1-4 Likert) Experts-CF clearly outperforms other methods

17. Advantages of the Approach Noise Experts introduce less natural noise Malicious Ratings Dataset can be monitored to avoid shilling Cold Start problem Experts rate items as soon as they are available Data Sparsity Reduced set of domain experts can be motivated to rate items

18. Conclusion Different approach to the Recommendation problem In some conditions, users prefer recommendations from similar experts than similar users. Expert-based CF has the potential to address many of standard CF shortcomings