1. Recommendation Systems By: Bryan Powell, Neil Kumar, Manjap Singh

2. Recommendation system? Information filtering technology Presents data on products that interests the user Algorithm uses previous user interactions

3. What does a recommendation system do exactly?  Observes apparent user characteristics  Compares characteristics to an item  Predicts a rating the user would give to the item  Assigns the highest predicted item as a recommendation

4. General Recommendation Types Personalized recommendation based on the individual's past behavior Social recommendation based on the past behavior of similar users Item recommendation based on the item itself

5. Amazon Amazon used all 3 approaches (personalized, social and item). Amazon’s recommendation system is very sophisticated

6. ALL MIGHTY GOOGLE Google uses its recommendation system every time a user searches through it. Based on your location and/or recent search activity When you're signed in to your Google Account, you “may see even more relevant, useful results based on your web history”

7. Google Cont. Google's search algorithm is called PageRank. Dependent on social recommendations (i.e. who links to a webpage) Google also does item recommendations with its “Did you mean” feature.

8. Who uses Recommendation Systems? Content Sites eCommerce Sites Advertisment

9. Content Sites Task: predict ratings of items by a given user find a list of interesting items Data: content description explicit rating for some user Examples: AlloCine, Zagat, LibraryThing, Last.fm, Pandora, StumbleUpon Recommendation for a user on LibraryThing

10. eCommerce Sites Task: build group of products for bundle sales find a list of products that a user is likely to buy Data: list of purchases browsing history for all users Example: Amazon Netfix

11. The Recommendation Giant http://www.netflix.com/

12. eCommerce Sites Cont. Netflix Prize $1 million prize given in 2009 Sought to substantially improve Netflix’s method of predictions for users

13. eCommerce Sites Cont. Netflix Challenge Cont. The BellKor’s Pragmatic Chaos team improved Netflix’s recommendation system by 10.06 % BellKor's Pragmatic Chaos

14. eCommerce Sites Cont. (Netflix Cont.) The BellKor’s Pragmatic Chaos team had a lower score than the 2 nd place team (The Ensemble) The Belkor’s Pragmatic Chaos team: (10.06%) The Ensemble: (10.06%) The Belkor’s Pragmatic Chaos only won because they submitted their code 20 minutes before The Ensemble..856714

15. Advertisement Task: find a list of advertisements optimized according to expected income Data: browsing history for all users Example: Google AdSense, DoubleClick

16. Common Approaches to Recommendation Systems Content Filtering Algorithms Collaborative Filtering Algorithms Hybrid Methods K-Nearest Neighbor Approach

17. Content Filtering Algorithms Algorithm based on attributes of items and ratings of the user Interprets the preferences of a user as a function of attributes Two main types of C.F.A.: Heuristic – Based Model Based

18. Content Filtering Algorithms Cont. Heuristic Based Uses common types of information retrieval TF/ ID Cosine Clustering Model Based Uses a probabilistic model to learn the predictions of a user

19. Collaborative Filtering Filters information/patterns using different sources Involves very large data sets Filters what the user sees based on tastes Steps: Look for users who share similar rating patterns Calculate predictions for user from other ratings Amazon invented item-based collaborative filtering

20. Collaborative Filtering Cont.

21. Hybrid Methods Uses both item attributes and the ratings of all users Hybrid methods were made to cope with the conventional recommendation system Two main types of C.F.A.: Heuristic – Based Model Based

22. Hybrid Methods Cont. Heuristic Based Uses both content filtering and collaborative filtering methods Aims to get the best from both algorithms Model Based Model is modified in order to take into account both types of data

23. K-Nearest Neighbor Approach Classified based on a majority of its neighbors Classifies Objects based on closest training examples Computation deferred until classification  instance- based learning Can be used for regression and utilizes Euclidean distances Larger “k” values reduce noise on classification They make boundaries between classification less distinct

25. Additional Resources Netflix Prize- http://www.netflixprize.com//community/viewtopic.php?id=1537 http://www.netflixprize.com//community/viewtopic.php?id=1537 uPenn- http://www.cis.upenn.edu/~ungar/CF/http://www.cis.upenn.edu/~ungar/CF/