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Recommendation Systems

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Recommendation Systems

  1. 1. Recommender System Mahmut Özge Karakaya
  2. 2. Recommender System Helps users find items of interests based on; ● Explicit ratings ● Past transactions ● Item content
  3. 3. Recommender System Process; ● Generate model ● Predict items ● Rank
  4. 4. History ● Tapestry (1992) ○ Subscribe mail lists ● ACM Recsys (2007 - ...) ● Netflix Prize (2009) ○ 1m$ prize
  5. 5. Amazon
  6. 6. goodreads
  7. 7. Google News
  8. 8. eHarmony
  9. 9. Every Domain is Unique ● Data ● Time ○ year ○ time of day ○ mood ○ item first appeared ● Item consumed before ● Whose opinion ● Cluster users
  10. 10. Solutions ● Apache Mahout ○ hadoop, open source ● Myrrix ○ hadoop, cloud, Cloudera ● easyrec ○ open source, restful ● LensKit ○ open source, movielens
  11. 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. 12. Drawbacks ● Personalized recommenders are difficult to set up ○ Algorithms ○ Scalability ● Maintenance ○ System ○ Monitoring ● Sometimes they’re wrong ● Attacks ○ Outliers
  13. 13. Taxonomy of Recommenders ● Content Based Filtering ● Collaborative Filtering ● Hybrid Recommenders
  14. 14. Content Based Filtering ● Knowledge Based Filtering ● Learn User Profile
  15. 15. Knowledge Based Filtering
  16. 16. Learn User Profile ● IMDb ●
  17. 17. Collaborative Filtering ● Memory Based ○ Item Based ○ User Based ● Model Based ○ SVD
  18. 18. Memory Based ● What is Similarity Matrix? ● Nearest neighbours
  19. 19. Memory Based ● Will Eric rent the movie Titanic?
  20. 20. Item Based
  21. 21. Item Based
  22. 22. Item Based ● Similarity ● Prediction
  23. 23. User Based
  24. 24. User Based ● Similarity ● Prediction
  25. 25. SVD ● Prediction
  26. 26. SVD ● Learning rule
  27. 27. Item Based vs User Based vs SVD ● Least memory: SVD ● Most accurate: SVD ● Explanation: Item Based
  28. 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. 29. Evaluation Setup ● Train Set (~%70) ● Test Set (~%30) ○ Probe Set
  30. 30. Taxonomy of Evaluation ● Predicting Ratings ● Recommending Items
  31. 31. Predicting Ratings ● Accuracy ○ Mae ○ RMSE
  32. 32. Taxonomy of Evaluation ● Recommending Items ○ Accuracy ● Recall ○ Diversity ○ Aggregate Diversity ○ Novelty
  33. 33. Recall
  34. 34. Diversity
  35. 35. Aggregate Diversity ● # of recommended unique items ● Long Tail
  36. 36. Novelty where; a is an item, u is number of users. is number of users who rated item a.
  37. 37. Fields of Study ● Algorithms ● Evaluation Metrics ● Cross-domain ● Group recommendations ● ...
  38. 38. Further Readings ● Recommender Systems Handbook ● https://www.coursera.org/course/recsys ● Mahout in Action ● ACM Conference Papers
  39. 39. Recommender System Thank you for listening

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