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Collaborative Filtering

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Presentation on theme: "Collaborative Filtering"— Presentation transcript:

1 Collaborative Filtering
Presentation by Alex Hugger

2 Departement/Institut/Gruppe
Filtering Documents Montag, 17. September 2018 Departement/Institut/Gruppe

3 Content-Based Methods
Find other popular items by the same author or similar keywords Recommendation quality is relatively poor Montag, 17. September 2018

4 Filtering Music Montag, 17. September 2018

5 Filtering Jokes Montag, 17. September 2018

6 Filtering Jokes Let the users rate the jokes Sort by average rating
Montag, 17. September 2018

7 Collaborative Filtering
People who have agreed in the past tend to agree in the future Montag, 17. September 2018

8 Good or Bad? Die Hard (1988) Dirty Dancing (1987)
Montag, 17. September 2018

9 Good or Bad? Montag, 17. September 2018

10 Montag, 17. September 2018

11 Jester 4.0 (http://eigentaste.berkeley.edu)
Montag, 17. September 2018

12 MovieLens (http://movielens.org)
Montag, 17. September 2018

13 Netflix www.netflix.org DVD/Blue-Ray rental and video streaming
1’000’000$ for the first beating the current recommendation algorithm by 10% Competition started in October 2006 Ended July 2009 Montag, 17. September 2018

14 GroupLens: An Open Architecture for Collaborative Filtering of NetNews
Research paper from 1994 by: Paul Resnick, MIT Center for Coordination Science Neophytos Iacovou, University of Minnesota Mitesh Suchak, MIT Center for Coordination Science Peter Bergstrom , University of Minnesota John Riedl , University of Minnesota Montag, 17. September 2018

15 NetNews Montag, 17. September 2018

16 Problems of NetNews Signal to noise ratio is too low
Splitting bulletin board into newsgroups Moderated newsgroups News clients Summary of the author and subject line Display discussion threads together String search facilities Kill files Montag, 17. September 2018

17 Modification to NetNews
2.61 3.72 Montag, 17. September 2018 Montag, 17. September 2018 17

18 Predicting Scores Score prediction system is robust to certain differences of interpretation of the rating scale One user rates 3-5 and the other 1-3 One thinks 1 and the other 5 is best score Montag, 17. September 2018

19 Predicting Scores Predictions can be modeled as matrix filling Item #
Ken Lee Meg Nan 1 4 2 5 3 6 ? Montag, 17. September 2018

20 Predicting Scores Assign similarities to each of the other people
Compute over articles rated by both Pearson Correlation Coefficients Between -1 and 1 = standard deviation of Ken = average of Ken’s ratings Montag, 17. September 2018

21 Predicting Scores Correlation Coefficients of Ken User Correlation Lee
-0.8 Meg +1 Nan # Ken Lee Meg Nan 1 4 2 5 3 6 ? Montag, 17. September 2018

22 Predicting Scores Weighted average of all ratings on article 6
Ken’s prediction is 4.56 4.56 Montag, 17. September 2018

23 Scaling Issues Relevant performance measures
Prediction quality Compute time and disk storage Rating is small, but each article may be rated by many users Volume of ratings could exceed volume of news Montag, 17. September 2018

24 Scaling Issues Pre-fetching ratings and pre-computing predictions keeps user time constant High computation complexity Volume of all ratings may exceed the storage capacity 100’000 users rate 10 articles per day. 100 bytes are required to store a rating. 1GB of storage required per 10 days. Montag, 17. September 2018

25 Cluster Models Montag, 17. September 2018

26 Cluster Models Better online scalability and performance than classical collaborative filtering Complex and extensive clustering is run offline Prediction quality gets reduced Montag, 17. September 2018

27 Item-to-Item Collaborative Filtering
Montag, 17. September 2018

28 Item-to-Item Collaborative Filtering
Amazon.com extensively uses recommendation algorithms 10’000’000 products and customers Result returned in real-time (< 0.5s) Algorithm must respond immediately to new information Montag, 17. September 2018

29 Amazon.com Montag, 17. September 2018

30 Amazon.com Montag, 17. September 2018

31 Amazon.com Montag, 17. September 2018

32 Amazon.com Montag, 17. September 2018

33 How It Works - Offline Similar-items table
Calculating similarity between a single product and all related products Complexity: O(mn2) - in practice: O(mn) m: number of users n: number of items Montag, 17. September 2018

34 How It Works - Online Given a similar-items table
Find all similar items to each of the users ratings and purchases Aggregate those items Recommend most popular and correlated items Number of users has no effect on performance Montag, 17. September 2018

35 General difficulties Cold start Self-fulfilling prophecy
Recommendations for groups Evaluation of recommendation systems Montag, 17. September 2018

36 Conclusion Effective form of targeted marketing
Mostly used in e-commerce business But can always be used when signal to noise ratio is too low Montag, 17. September 2018

37 Questions? Montag, 17. September 2018

38 References GroupLens: An Open Architecture for Collaborative Filtering of NetNews Published 1994 Paul Resnick, MIT Center for Coordination Science Neophytos Iacovou, University of Minnesota Mitesh Suchak, MIT Center for Coordination Science Peter Bergstrom , University of Minnesota John Riedl , University of Minnesota Amazon.com Recommendations Published 2003 Greg Linden Brent Smith Jeremy York Montag, 17. September 2018

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