Tagommenders : Connecting Users to Items through Tags

1. Tagommenders: Connecting Users to Items through Tags Written by Shilad Sen, Jesse Vig, and John Riedl (2009) Presented by Ken Hu and Hassan Hattab

2. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

3. First, Recommenders.  • What is Recommender system? • Two Main tasks • Recommend. • Predict.

4. Recommender Systems  • Types of recommender systems: • User-based: decides according to the user's previous choices • Item-based: decides according to related items to a selected item • SVD • Problem: These methods don't consider the content of the item.  • Solution: Content-based Recommenders 

5. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

6. Tagging Systems    • Uses tags to address (categorize) items to users • Tags are created by general users (More meaningful )

7. Tagommenders:  • Basically, they combine Recommenders (content-based) and tagging systems.  • Two main parts for Tagommenders: • They infer users’ preferences for tags based on their interactions with tags and movies • and they infer users’ preferences for movies based on their preferences for tags.

8. Tagommender's data set These are collected from the MovieLens website. • Movie Rating  • Movie clicks • Tag applications • Tag Searches  •  Tag Preference Ratings

9. Tagommender's data set

10. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

11. Tagommender's  Cycle

12. Inferring Tag Preference • Inferring Preference using Tag Signals (Direct) 

13. Inferring Tag Preference • Inferring Preference using Item Signals (indirect) 

14. Inferring Preference using Item Signals • Sigmoid transformation is used to calculate the weight of movie m to tag t

15. Inferring Preference using Item SignalsMethods • Movie-Clicks • Movie-log-odds-clicks • Movie-r-Clicks • Movie-r-log-odds-clicks • Movie-Rating • Movie Bayes

16. 1- Movie-Clicks: set of movies clicked by user u

17. 2- Movie-log-odds-clicks

18. 3- Movie-r-Clicks4- Movie-r-log-odds-clicks • The only difference is Movie-rating is counted rather than movie clicks 

19. 5- Movie-Rating • A user’s preference for a tag is the average rating for a movie under that tag.  user u's rating for movie m

20. 6- Movie-bayes • A bayesian generative model for users rating for a certain tag. • if the tag is relevant to a rating then the rating will be chosen from the user-tag-specific distribution • Else, it will be chosen from the user background rating  distribution

21. Which one is better?

22. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

23. Recommenders • Explicit Algorithms • Use users' movie ratings • Recommend and predict • 3 algorithms • Cosine-tag • Linear-tag • Regress-tag Implicit Tag data only Recommend only 2 algorithms Implicit-tag Implicit-tag-pop

24. Implicit : Implicit-tag Vector Space Model Inferred tag preference Relevance weight

25. Implicit : Implicit-tag-pop Implicit-tag with movie popularity Tag > clicks, clicker count > click count Linear estimation of log function

26. Recommenders • Explicit Algorithms • Use users' movie ratings • Recommend and predict • 3 algorithms • Cosine-tag • Linear-tag • Regress-tag Implicit Tag data only Recommend only 2 algorithms Implicit-tag Implicit-tag-pop

27. Explicit : Cosine-tag Cosine similarity: rating vs tag preference

28. Explicit : Linear-tag Least-square fit linear regression

29. Explicit : Regress-tag Linear-tag with similarity between tags  SVM was best to estimate h Robustness against overfitting

30. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

31. Results : Background • Competitors • Overall-avg • User-avg • User-movie-avg • Explicit-item • Implicit-item • Funk-svd • Hybrid • Regress-tag + funk-svd Comparisons Top-5 Compare top five recommendations MAE Average error of prediction

32. Results : Top-5

33. Results : MAE

34. Overview • Recommenders • Implicit • Explicit • Results • Conclusion Introduction Tagommender Philosophy Dataset Tag Preference Inference Approach Methods

35. Conclusion Introduced recommender algorithms based on user suggested tags (Tagommenders) Best at recommendation tasks Adds value at prediction tasks Hybrid predictors does very well Other advantages Ease to explain Algorithmic evaluation of tag quality

36. Questions?