1. The interplay of personal preference and social influence in sharing networks Amit Sharma Ph.D. candidate, Dept. of Computer Science Cornell University B-Exam 2. Whose favorite was Friday (and why)? 2 3. Sharing networks are popular 3 4. Sharing networks are popular 4 5. Sharing networks are popular 5 6. Sharing networks are popular 6 7. Two ways of looking at the world 7 RECOMMENDATION SYSTEMS Model individual preferences of users Does not consider explicitly the effect of sharing or social processes [Ma et al. 09, Konstas et al. 08, Jamali and Ester 10, Sharma and Cosley 11, Sharma and Yan 13] NETWORK DIFFUSION Model spread of items in a social network, one at a time Does not consider an individuals preferences over items [Watts 02, Kempe et al. 03, Bakshy et al. 09, Lerman and Ghosh 10 ] 8. Towards understanding peoples adoption and sharing decisions 8 Interplay between people's own preference, social influence and system elements such as recommendations or feeds. Preference models Influence estimation System context 9. Example 1: How do friends-based recommendations compare with those computed from the full network? 9 [Sharma-Gemici-Cosley 2013] MOVIES MUSIC HASHTAGS # Friends ~ 100-500 # Non-friends ~ 50k 10. Example 2: Which social explanation would influence you to try out a musical artist? 10 10 of your friends like this Dan and Levent like this a = 0.61 a = 0.74a = 0.66 10 of your friends like this. Dan likes this. [Sharma-Cosley 2013] MUSIC a = Rigidness 11. My contributions EXPERIMENTAL The effect of personal preference and influence on peoples decisions Item adoption [WWW 13] Item sharing [CSCW 15] 11 OBSERVATIONAL Aggregate effect of peoples activities over the sharing network Extent of preference locality [ICWSM 13] Influence estimation [In submission to CSCW 16] 12. 12 Understanding peoples adoption and sharing decisions How do peoples preferences influence their sharing decisions? How much do social feeds influence peoples actions on items? Future Work Outline 13. Directed sharing: Questions Why did she share that item? Does she like it? Will he like it? Can we predict what items she will share to him? 13 14. Two motivations for sharing Word-of-mouth Individuation Establish a distinct identity for oneself Altruism Help others [Dempsey et al. 2010] Online Content sharing Senders preferences Sender shares what she likes Recipients preferences Sender shares what recipient would like Comparing senders rating versus recipients rating for a shared item can indicate the relative effect of these motivations. 14 15. Research questions RQ1: To what extent do people tend to share items that they like themselves (individuation) versus those that they perceive to be relevant for the recipient (altruism)? RQ2: Can we predict whether an item is shared based on senders and recipients preferences? 15 16. Person As movie Likes Compute recs. Person Bs movie Likes Compute recs. Combine recs. A paired experiment on Facebook (N=118) 16 17. 10 Recs. for me 10 Recs. for partner To mitigate social influence effects, my partner is not shown which movies were shared by me. 17 Own-Algo Other-Algo MOVIES 18. People share what they like themselves Rating by Senders Frequency 18 19. Senders rate shared items higher than recipients Mean sender rating: 4.19 Mean recipient rating: 3.88 (Paired t-test) Sender Rating Recipient Rating Frequency 19 20. Responses support individuation Usually when I suggest, it depends on the item, not the target individual, because I want to share what I enjoyed. (P8) I suggest because I like something and I want to see if other people feel the same way about an item. (P91) Altruism: I make suggestions to people if I think they might gain enjoyment. Obviously it really depends on their personality and their likes/dislikes. (P22) 20 21. Data from people who did not see all recommendations Due to lack of Like data or API errors. Recs. for me Recs. for partner Recs. for me Recs. for partner Both-Shown Other-ShownOwn-Shown 21 22. Ratings for shared items depend on item set shown Recs. for me Recs. for partner Other-ShownOwn-Shown 22 Recs. for me Recs. for partner Both-Shown Senders = 4.4 Recipients = 3.7 (***) Senders = 4.06 Recipients = 4.28 (ns) Own-Algo: (***) Other-Algo: (ns) [Paired t-test] Salience of items impacts what gets shared. 23. A preference-salience model I try to assess if the individual that I am recommending to would like the movie that I am suggesting. Otherwise, I do not tell them about the movie, and may think of someone else who would like the movie. (P5) Peoples own preferences determine shareable items. Among these candidates, some become salient based on the context. They are shared if sharer thinks they are suitable for the recipient. 23 24. Other plausible models High Quality Model No difference between overall IMDB ratings for shared and non- shared movies. Misguided Altruism Model Senders ratings are higher for shares than non-shares. 24 25. RQ2: Can we predict what is shared? Classification task: Given a sender, recipient and an item, decide whether it was shared or not. Randomly sampled an equal number of non-shares. Use 10-fold cross validation and a decision tree classifier. Evaluation metrics: Precision and recall. Features: IMDB average rating, popularity for item Recipients predicted rating for item Senders predicted rating for item 25 26. Better precision with sender-based features 0 10 20 30 40 50 60 70 80 90 IMDB Rating Popularity Recipient-Item Rating Sender-Item Rating All Precision Recall 26 27. Summary RQ1: Individuation (personal preferences) dominate the decision process for directed content sharing. RQ2: Based on sender and recipient preferences, we can (noisily) predict what is shared. As with adoption (Example 2), peoples own preferences dominate their sharing decisions. 27 28. 28 Understanding peoples adoption and sharing decisions Peoples personal preferences dominate their sharing decisions How much do social feeds influence peoples actions on items? Future Work Outline 29. How much and why do people copy feed actions? 29 Virality is rare, vast majority of shares spread to zero or one degree [Goel et al. 12]. Most studies on social media find a nontrivial correlation between the activities of a user and her friends [Sharma and Cosley 13]. Q: Can we ascribe how many copy actions are caused by influence from friends? In general, hard to infer from observational data alone [Shalizi and Thomas 11]. 30. Many processes for generating a common action by friends Social Influence Homophily 30 Without controlling for homophily, we may overestimate influence [Aral et al. 09, Lewis et al. 12]. 31. A testable definition for influence Influence: Deviation from the expected activity based on following ones personal preference for items. Based on a data-driven model of personal preferences. Past actions represent a users preferences. Based on an explicit model of the system elements that exposes people to others activities. Assume reverse chronological feed. A user scans it from top to bottom. 31 32. Controlling for homophily using preference similarity Use observed activity to create a proxy for homophily. 32 Non-FriendsFriends f5 u f1 f4 f3f2 n5 u n1 n4 n3n2 0.4 0.4 0.70.3 0.60.5 0.7 0.3 0.60.5 33. Estimating the actions due to influence For each action by a user, construct feeds from friends and non-friends containing their last M actions respectively. Friends Overlap = Fraction of actions done by u that are also in the friends Feed (Nave measure of influence [Ghosh et al. 10, Bakshy et al. 11]). NonFriends Overlap = Fraction of actions done by u that are also in the non-friends Feed. 33 34. The full procedure MATCHING STEP (before time T) For each user: Construct a set of non-friends that are as similar to the user as her friends. ESTIMATION STEP (after time T) For each user: Influenceu = FriendsOverlap NonFriendsOverlap 34 35. The Last.fm dataset 35 LISTEN SONG LOVE SONG # Ego Networks 96K # Total Users 312K # Total Songs 23M # Total Actions 656M # Ego Networks 141K # Total Users 437K # Total Songs 13M # Total Actions 140M Size of Feed(M) = 10 Time T is chosen such that 90% of actions are before T. Random seeds, Weighted breadth-first crawl for 3 months 36. Validation using semi-synthetic Loves data 36 Personal preference: Choose a song randomly from the last M loves by the k-most similar users (k=10). Influence process: Choose a song randomly from the last M loves by her friends. Process FriendsOverlap Influence Std. Error Personal Preference(PP) 0.042 0.001 0.0001 Influence(I) 1.00 0.99 0.0004 I-PP(10%-90%) 0.15 0.102 0.0001 Generate synthetic loves on songs after time T from any of the processes, keeping the timestamps and the social network same as before. 37. FriendsOverlap overestimates influence by at least 300% across listen and love actions. 37 38. Is this specific to Last.fm? 38 Assumptions of Influence Estimation: Reverse chronological feed Preferences as a proxy for homophily Can be applied to any sharing platform that shows friends activities in a (loosely) reverse chronological order. RATE BOOKS FAVORITE PHOTOS RATE MOVIES # Ego Networks 252K # Total Users 252K # Total Items 1.3M # Total Actions 28M # Ego Networks 49K # Total Users 50K # Total Items 48K # Total Actions 7.9M # Ego Networks 175K # Total Users 183K # Total Items 11M # Total Actions 33M [Huang et al. 12] [Jamali and Ester 10] [Cha et al. 09] 39. FriendsOverlap overestimates influence in all three domains 39 Overestimate by 14% in Flickr, more than 500% in Flixster. 40. Influence is overrated(?) 40 Not more than 1% of user actions on online sharing networks can be attributed to influence. 41. 41 Understanding peoples adoption and sharing decisions Peoples personal preferences dominate their sharing decisions Less than 1% of peoples actions due to social influence. Future Work Outline 42. Claim: Modeling both preference and influence leads to better understanding of diffusion 42 Improves estimates of the effect of social influence Suggests personalization strategies for social recommenders Points to preference-aware models of diffusion 0 20 40 60 80 100 IMDB Rating Recipient-Item Rating Sender-Item Rating Precision Recall 43. Future Work 43 RECOMMENDATION SYSTEMS Reason about properties such as influence, diversity, utility and privacy. Build recommendation algorithms that account for these properties. NETWORK DIFFUSION Develop diffusion models that incorporate peoples preference and social influence. Evaluate by accuracy in predicting adoptions or shares. 44. Thank you 44 RECOMMENDATION SYSTEMS Reason about properties such as influence, diversity, utility and privacy. Build recommender algorithms that account for these properties. NETWORK DIFFUSION Develop diffusion models that incorporate peoples preference and social influence. Evaluate by accuracy in predicting adoptions or shares. Collaborators: Dan Cosley, Mevlana Gemici, Michael Triche, Yulan Miao, Meethu Malu 45. Directed sharing: More altruism? Meformers versus informers: ~80% of content shared on Twitter was about the user [Naaman et al. 2008] In directed sharing, there is a known recipient Expect altruism to be more important 45 46. Design implications Recommender systems for effective sharing Recommending what to share, who to share it to. E.g., Feedme system [Bernstein et al. 2010] Diffusion models with directed sharing Accounting for sender and recipient preferences 46 47. Decision Tree for sharing 47 48. Decision Tree for sharing 48 49. Influence per user shows a starker contrast For loves on songs more than 50% of users have zero or lower influence estimate. For loves on artists, influence increases but accounting for preference similarity still cancels out most of FriendsOverlap. 49 50. A simple decision model for ratings User's receptiveness to an explanation. [Effect of Explanation] User's discernment in music. [Base Decision Process] Coldplay + Amit Sharma likes this. 50 51. A simple decision model for ratings Base Decision Process f(x) = D e-Dx D: Discernment Effect of Explanations Mixture Model h(x) = a f(x) + (1-a) g(x) a: Rigidness : Receptivity 51 52. Model explains variance in peoples ratings 52 a = 0.61 a = 0.74a = 0.71 a = 0.66a = 0.66 53. People are differently susceptible to explanation 53 Recommender Systems: Opportunities for personalized explanation strategies. User Cluster 1 User Cluster 2 User Cluster 3 User rating User rating User rating Probability 54. Future work Temporal evolution of preference All friends arent equal Exploring new domains, towards general models of behavior Feedback between experimental and observational studies 54 55. Implementing the test on real network data 55 . Core user: Any user for whom at least 75% of friends have preferences in our dataset. 56. Datasets from Facebook and Twitter Activity data: Movie and music Likes on Facebook, hashtag usage on Twitter 56 57. Datasets 57