1. Recommender
System
Mahmut Özge Karakaya

2. Recommender System
Helps users find items of interests based on;
● Explicit ratings
● Past transactions
● Item content

3. Recommender System
Process;
● Generate model
● Predict items
● Rank

4. History
● Tapestry (1992)
○ Subscribe mail lists
● ACM Recsys (2007 - ...)
● Netflix Prize (2009)
○ 1m$ prize

5. Amazon

6. goodreads

7. Google News

8. eHarmony

9. Every Domain is Unique
● Data
● Time
○ year
○ time of day
○ mood
○ item first appeared
● Item consumed before
● Whose opinion
● Cluster users

10. Solutions
● Apache Mahout
○ hadoop, open source
● Myrrix
○ hadoop, cloud, Cloudera
● easyrec
○ open source, restful
● LensKit
○ open source, movielens

11. Benefits
● Increase on sales
○ Amazon 2006 %35
● Based on real activity
○ Always Up-To-Date
● Great for discovery
● Right item to right user
● Personalization
● Reduced organizational maintenance
○ Navigation

12. Drawbacks
● Personalized recommenders are difficult to
set up
○ Algorithms
○ Scalability
● Maintenance
○ System
○ Monitoring
● Sometimes they’re wrong
● Attacks
○ Outliers

13. Taxonomy of Recommenders
● Content Based Filtering
● Collaborative Filtering
● Hybrid Recommenders

14. Content Based Filtering
● Knowledge Based Filtering
● Learn User Profile

15. Knowledge Based Filtering

16. Learn User Profile
● IMDb
●

17. Collaborative Filtering
● Memory Based
○ Item Based
○ User Based
● Model Based
○ SVD

18. Memory Based
● What is Similarity Matrix?
● Nearest neighbours

19. Memory Based
● Will Eric rent the movie Titanic?

20. Item Based

21. Item Based

22. Item Based
● Similarity
● Prediction

23. User Based

24. User Based
● Similarity
● Prediction

25. SVD
● Prediction

26. SVD
● Learning rule

27. Item Based vs User Based vs SVD
● Least memory: SVD
● Most accurate: SVD
● Explanation: Item Based

28. Content Based vs Collaborative
● Least memory: Content Based
● Least learning: Content Based
● No content needed: Collaborative
● Cold start: Collaborative
● Social: Collaborative
● Shortest Prediction Time: Collaborative

29. Evaluation Setup
● Train Set (~%70)
● Test Set (~%30)
○ Probe Set

30. Taxonomy of Evaluation
● Predicting Ratings
● Recommending Items

31. Predicting Ratings
● Accuracy
○ Mae
○ RMSE

32. Taxonomy of Evaluation
● Recommending Items
○ Accuracy
● Recall
○ Diversity
○ Aggregate Diversity
○ Novelty

33. Recall

34. Diversity

35. Aggregate Diversity
● # of recommended unique items
● Long Tail

36. Novelty
where;
a is an item, u is number of users. is
number of users who rated item a.

37. Fields of Study
● Algorithms
● Evaluation Metrics
● Cross-domain
● Group recommendations
● ...

38. Further Readings
● Recommender Systems Handbook
● https://www.coursera.org/course/recsys
● Mahout in Action
● ACM Conference Papers

39. Recommender System
Thank you for listening