Analyzing Weighting Schemes in Collaborative Filtering:     Cold Start, Post Cold Start and Power Users                   ...
OutlineMovie RecommendationProblem: Popularity BiasCollaborative FilteringSimilarity Weighting SchemesExperimentsResultsCo...
Recommender SystemsWhat they should do:   Find items which should be of interest to users   Find items which should be use...
Popularity Bias                                 What is popularity bias?Some things are more popular than others      Bloc...
Popularity Bias30.03.2012                     5
Popularity Bias                        ed                    rat                hly         =  hig      ms    ite    lar  ...
Collaborative FilteringLooks for users who share rating patternsUse ratings from like-minded users to calculate a predicti...
Collaborative Filtering: SimilaritiesStandard CF approaches do not consider the popularity of items when creating neighbor...
Collaborative Filtering: SimilaritiesStandard CF approaches do not consider the popularity of items when creating neighbor...
Weighting Schemes30.03.2012                       10
ExperimentsApproach: Test two similarity weighting strat- egies in different scenarios on two different movie rating datas...
Results30.03.2012             12
ResultsWhen is it good to use popularity weighting?                      Movielens 10M             >20% improvement in Pre...
ResultsWhen is it not good to use popularity weighting?                       Moviepilot             No significant improv...
ConclusionPopular items create a problem for recom- mender systems due to favorable bias.Similarity weighting can lessen t...
Ongoing WorkWhat if lower precision does not mean poorer quality?   Lower precision can be an indicator of new, novel,    ...
Questions?             Thank You!30.03.2012                17
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Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users

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Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users

  1. 1. Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users ACM SAC 2012 Alan Said, Brijnesh J. Jain, Sahin Albayrak {alan, jain, sahin}@dai-lab.de TU-Berlin
  2. 2. OutlineMovie RecommendationProblem: Popularity BiasCollaborative FilteringSimilarity Weighting SchemesExperimentsResultsConclusion30.03.2012 2
  3. 3. Recommender SystemsWhat they should do: Find items which should be of interest to users Find items which should be useful to usersWhat they often do instead: Find items which are known by users Find items which users would have found anyway30.03.2012 3
  4. 4. Popularity Bias What is popularity bias?Some things are more popular than others Blockbuster movies1: Pulp Fiction, Inception, etc. Best selling books2: Steve Jobs Bio, A Song of Ice and Fire, etc. Apps3: Angry Birds, Skype, Kindle1 : IMDb most popular2 : Amazon 2011 best sellers3 : Most downloaded Android apps30.03.2012 4
  5. 5. Popularity Bias30.03.2012 5
  6. 6. Popularity Bias ed rat hly = hig ms ite lar puPo 30.03.2012 6
  7. 7. Collaborative FilteringLooks for users who share rating patternsUse ratings from like-minded users to calculate a prediction for the userBoils down to: The most similar users create a neighborhood. Those items which are most popular in the neighborhood will be recommended.30.03.2012 7
  8. 8. Collaborative Filtering: SimilaritiesStandard CF approaches do not consider the popularity of items when creating neighbor- hoods of similar users. i.e. not considering the popularity bias. Percentage of ratings given to different popularity classes of movies in the Movielens 10 Million ratings dataset30.03.2012 8
  9. 9. Collaborative Filtering: SimilaritiesStandard CF approaches do not consider the popularity of items when creating neighbor- hoods of similar users. i.e. not considering the popularity bias. Disitribution of ratings given to the three most popular movies in the Movielens 10 Million dataset30.03.2012 9
  10. 10. Weighting Schemes30.03.2012 10
  11. 11. ExperimentsApproach: Test two similarity weighting strat- egies in different scenarios on two different movie rating datasets. Weighting: Linear Inverse & Inverse User frequency Datasets: Movielens10M & Moviepilot Scenarios: Cold Start, Post Cold Start, Power Users30.03.2012 11
  12. 12. Results30.03.2012 12
  13. 13. ResultsWhen is it good to use popularity weighting? Movielens 10M >20% improvement in Precision Ratings: 1-5 stars ← 30 - 100 items each → 1330.03.2012
  14. 14. ResultsWhen is it not good to use popularity weighting? Moviepilot No significant improvement in Precision Ratings: 0-10 stars30.03.2012 14
  15. 15. ConclusionPopular items create a problem for recom- mender systems due to favorable bias.Similarity weighting can lessen the effects of the bias when the rating scale is “compact” when the users have “more than few” and “less than many” ratings30.03.2012 15
  16. 16. Ongoing WorkWhat if lower precision does not mean poorer quality? Lower precision can be an indicator of new, novel, serendipitous recommendations – these will produce lower precision values in offline evaluation Currently evaluating the quality of recommender algorithms based on user feedback, not only precision/recall/etc. Values. Users and Noise: The Magic Barrier of Recommender Systems – UMAP12 User satisfaction survey: www.dai-lab.de/~alan/survey30.03.2012 16
  17. 17. Questions? Thank You!30.03.2012 17

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