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Recommendation Systems

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  • 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

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