1. Khalid El-Arini Carnegie Mellon University Joint work with: Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas Transparent User Models for Personalization

2. Personalization is ubiquitous.

3. YouTube: 72+ hours/minute of new video Facebook: 950 million+ users Twitter: 400+ million tweets/day Shopping: [1994]: 500K unique consumer goods sold in U.S. [2010]: Amazon alone offered 24 million. 3 Personalization is invaluable. Keyword search is not enough.

4. Personalization is often wrong.

5. - J. Zaslow, November 26, 2002 “Basil…is not a neo-Nazi. Lukas…is not a shadowy stalker. David…is not Korean. intent on giving them such labels.”

6. “there's just one way to change its mind: outfox it.” - J. Zaslow, November 26, 2002 What recourse do we have? Can we do better?

7. You behave like a vegan hipster Vegan? Really? Why? You: tweeted with #meatlessmonday follow @WholeFoods … We propose an alternative. Why am I getting this?

8. We propose an alternative. Why am I getting this? You behave like a Brooklyn hipster Goal: Achieve transparency via interpretable user features, learned from user activity

9. You behave like a Brooklyn hipster Goal: Achieve transparency via interpretable user features, learned from user activity Badges

10. 10 ApproachModelExperimentsSummary

11. 11 1. Define a vocabulary of badges Apple fanboy … veganrunnerphotographer Rich, interpretable and explainable

12. 12 1. Define a vocabulary of badges 2. Identify exemplars How do I find vegans?

13. 13 observed label Take advantage of how users describe themselves Take advantage of how users describe themselves

14. 14 Most vegans don’t label themselves as “vegan” on Twitter… we want to infer the attributes of these users

15. 15 1. Define a vocabulary of badges 2. Identify exemplars 3. Model characteristic behavior Hashtags#meatlessmonday Retweets RT @WholeFoods

16. 16 ApproachModelExperimentsSummary

17. We have no negative training examples. Use a generative model. Actions can be explained by multiple badges, even for the same user. Noisy-or to combine badges. How do we deal with user corrections? Observing a latent variable. Model sketch

18. 18 i=1…B B badges

19. 19 u=1…N i=1…B N users

20. 20 u=1…N i=1…B F actions j=1…F

21. 21 b i (u) u=1…N i=1…B Does user u have badge i? j=1…F

22. 22 b i (u) λ i (u) u=1…N i=1…B j=1…F Does user u have label for badge i in his profile?

23. 23 a j (u) b i (u) λ i (u) j=1…F u=1…N i=1…B Has user u performed action j? j=1…F

24. 24 s ij a j (u) b i (u) λ i (u) j=1…F u=1…N i=1…B Does badge i explain action j?

25. 25 s ij φ ij a j (u) b i (u) w i (u) αφαφ βφβφ j=1…F u=1…N i=1…B What’s the probability that a user with badge i performs action j?

26. 26 s ij φ ij φ bg a j (u) b i (u) w i (u) αφαφ βφβφ j=1…F u=1…N i=1…B What is the background probability for each action?

27. 27 s ij φ ij φ bg a j (u) b i (u) w i (u) αφαφ βφβφ j=1…F u=1…N i=1…B noisy or: Can at least one of my badges (or the background) explain it? noisy or: Can at least one of my badges (or the background) explain it?

28. 28 s ij φ ij φ bg a j (u) b i (u) λ i (u) αφαφ βφβφ j=1…F u=1…N i=1…B

29. 29 s ij φ ij φ bg a j (u) b i (u) λ i (u) αφαφ βφβφ j=1…F u=1…N i=1…B Beta priors to control sparsity

30. 30 s ij φ ij φ bg a j (u) b i (u) λ i (u) γiTγiT γiFγiF αφαφ βφβφ αTαT βTβT αFαF βFβF j=1…F u=1…N i=1…B Beta prior to encode low recall (e.g., 10%) Beta prior to encode high precision (e.g., 99.9%) Beta prior to encode high precision (e.g., 99.9%)

31. 31 ηiηi s ij φ ij φ bg a j (u) b i (u) λ i (u) γiTγiT γiFγiF ωiωi αφαφ βφβφ αηαη βηβη αωαω βωβω αTαT βTβT αFαF βFβF j=1…F u=1…N i=1…B

32. Collapsed Gibbs sampler (with MH steps) 32 Inference s ij φ ij φ bg b i (u)

33. 33 ηiηi s ij φ ij φ bg a j (u) b i (u) λ i (u) γiTγiT γiFγiF ωiωi αφαφ βφβφ αηαη βηβη αωαω βωβω αTαT βTβT αFαF βFβF j=1…F u=1…N i=1…B You behave like a veganhipster.

34. 34 ηiηi s ij φ ij φ bg a j (u) b i (u) λ i (u) γiTγiT γiFγiF ωiωi αφαφ βφβφ αηαη βηβη αωαω βωβω αTαT βTβT αFαF βFβF j=1…F u=1…N i=1…B You behave like a veganhipster.

35. 35 ApproachModelExperimentsSummary

36. Start with 7 million Twitter users Manually define 31 sample badges by specifying labels 36 Data description

37. Start with 7 million Twitter users Manually define 31 sample badges by specifying labels Gather 2 million tweets from August 2011 Recall: actions are hashtags and retweets Remove infrequent actions and inactive users, leaving us with: 75,880 users 32,030 actions Data description

38. 38 artist photographer country music fan book worm Badge statistics

39. 39 Can we learn badges?

40. 40 Vegetarian badge

41. 41 Runner badge

42. 42 Hacker badge

43. 43 Manchester United badge

44. 44 Do all badges look this good? No, but most do.

45. 45 wine lover Over-generalized

46. 46 Overwhelmed Ruby on Rails

47. 47 Can we just use the labels directly?

48. 48 Inferred Apple fanboy badge Self-described Apple fanboys

49. Compare to labeled LDA [Ramage+ 2009] –LDA extension where each document is labeled with multiple tags –One-to-one mapping between topics and tags –Document explained only by topics associated with its tags Hold out random 10% of labels, treat as ground truth, and try to predict them 49 Comparative Analysis

50. 50 Rank of held-out labels better Better predictive performance Better predictive performance

51. 51 better Better predictions for active users

52. 52 Sparse badges Apple fanboy (badges)Apple fanboy (l-lda)

53. 53 ApproachModelExperimentsSummary

54. 54 Leveraged how users describe themselves

55. 55 Leveraged how users describe themselves to build interpretable user features You behave like a vegan hipster

56. 56 Empirically showed we can infer a user’s attributes from his behavior

57. 57 谢谢

58. What recourse do we have? Collaborative filtering Content-based filtering Can we do better?

59. 59 Most vegans don’t label themselves as “vegan” on Twitter… …but what about non-vegans? “I drink too much and hate vegans.”