Picking the Best                                         of the Best                                         Shlomo Berkov...
Information overloading problemWorkshop on Social Recommender Systems   UMAP-2012 Montreal   2
Social media looked like a solution•  Explicit expression of interests       –  Subscription       –  RSS       –  Friendi...
Recent Facebook statistics•  Average Facebook user       –  … has 130 friends       –  … is connected to 80 communities, g...
Activity/news/network feed•  What do they mean by feeds       –  A collection of discussions or headlines that are        ...
Activity feedsWorkshop on Social Recommender Systems   UMAP-2012 Montreal
Facebook I•  Dynamically providing a news feed about a user   of a social network (USP 7,669,123)       –  A method for di...
Facebook II•  Generating a feed of stories personalized for   members of a social network (USP 7,827,208)       –  A metho...
Push Back!       –  Without consulting us, Facebook filters its newsfeed            based on what content it thinks we wan...
What are items of interest?•  Works that address this issue:       –  Gilbert, E., Karahalios, K.: Predicting Tie Strength...
[Gilbert and Karaholios, 2009]•  Predict the strength of ties       –  Facebook platform•  70 features from 7 tie strength...
[Gilbert and Karaholios, 2009]•  Tie strength = linear combination of the 70   features       –  Regression model to deter...
[Gilbert and Karaholios, 2009]•  Categories                            •  Features•  Predictive error = 10%Workshop on Soc...
[Wu et al., 2010]•  Predict professional and personal closeness       –  Corporate intranet social network•  60 features f...
[Wu et al., 2010]•  Professional and personal closeness = linear   combination of the 60 variables       –  Regression mod...
[Wu et al., 2010]•  Predictive categories for each closeness•  Predictive error       –  18% error for professional closen...
[Paek et al., 2010]•  Predict the importance of news feed posts and   interest in activities of others       –  Facebook p...
[Paek et al., 2010]•  Linear SVM classifier using Sequential Minimal   Optimization•  Ground truth       –  24 participant...
[Freyne et al., 2010]•  Recommend news feed items       –  Corporate intranet social network•  Implicit feed item relevanc...
[Freyne et al., 2010]•  Ground truth       –  1800 feed clicks              •  Actual feed reconstructed              •  F...
[Guy et al., 2011]•  Recommend news feed items       –  Corporate intranet social network•  User profiling: explicit selec...
[Guy et al., 2011]•  Ground truth       –  126 users       –  Up to 10 people/terms/places of interest selected       –  5...
[Berkovsky et al., 2012]•  Re-rank news feed items       –  Health related social network•  Linear combination of implicit...
[Berkovsky et al., 2012]•  Rank of clicked feed items                            35%                                      ...
Synthesis   Paper                Social                Model        Individual/            Explicit/   Evaluation         ...
Where to next?•  Feed item scoring       –  Process and interpret content beyond text       –  Combine interest scores for...
Shameless Self-Promotion8th International Conference on Persuasive Technology     Sydney AU, 3-5 April 2013, http://pt2013...
Questions?                                         Discussion?                                                     Thank Y...
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  1. 1. Picking the Best of the Best Shlomo Berkovsky (NICTA)Workshop on Social Recommender Systems UMAP-2012 Montreal
  2. 2. Information overloading problemWorkshop on Social Recommender Systems UMAP-2012 Montreal 2
  3. 3. Social media looked like a solution•  Explicit expression of interests –  Subscription –  RSS –  Friending•  Done by users –  Always accessible –  Scrutable •  Can be adapted and modified•  Filter the incoming information streamWorkshop on Social Recommender Systems UMAP-2012 Montreal 3
  4. 4. Recent Facebook statistics•  Average Facebook user –  … has 130 friends –  … is connected to 80 communities, groups, and events –  … contributes 3 pieces of content every day –  … spends less than 50 minutes every day•  Quick math –  (130+80) * 3 / 24hr à contribution every 2min17sec –  (130+80) * 3 / 50min à need to view 12.6 contribution per minute•  Can you stay on top of this? –  What can help?Workshop on Social Recommender Systems UMAP-2012 Montreal 4
  5. 5. Activity/news/network feed•  What do they mean by feeds –  A collection of discussions or headlines that are published for distribution to the general public [FreeDictionary] –  A document whose discrete content items include web links to the source of the content [Wikipedia] –  A collection of events that is intended to give you a quick look at what your friends have been doing on Facebook [Facebook] –  A message that provides updates about items of interest based on custom notification settings … includes updates about changes to content, status of colleagues, social tags, profiles [ExpertGlossary]Workshop on Social Recommender Systems UMAP-2012 Montreal 5
  6. 6. Activity feedsWorkshop on Social Recommender Systems UMAP-2012 Montreal
  7. 7. Facebook I•  Dynamically providing a news feed about a user of a social network (USP 7,669,123) –  A method for displaying a news feed in a social network environment, the method comprising: •  monitoring activities in a social network environment; •  storing the activities in a database; •  generating news items regarding the activities, wherein the news items are for presentation to users and relate to activities that were performed by another user; •  attaching a link associated to the activities of another user to the news items where the link enables a viewing user to participate in the same activity as the another user; •  limiting access to the news items to the viewing users; and •  displaying a news feed comprising the plurality news items to the viewing users.Workshop on Social Recommender Systems UMAP-2012 Montreal 7
  8. 8. Facebook II•  Generating a feed of stories personalized for members of a social network (USP 7,827,208) –  A method for generating a personalized story for a viewing user, comprising: •  accessing relationship data between users; •  associating actions with users to produce consolidated data, identifying an action and a user who performed the action; •  identifying the elements of the consolidated data; •  producing aggregated data, identifying actions with a common element, a user who performed the action, and other users who performed actions with the common element; •  generating a story for the viewing user, comprising the action, the user who performed the action, and other users who performed an action with the common element; and •  sending the story for display to the viewing user.Workshop on Social Recommender Systems UMAP-2012 Montreal 8
  9. 9. Push Back! –  Without consulting us, Facebook filters its newsfeed based on what content it thinks we want to see. Based on our behavior, "likes”, and clicks, it removes content in which it believes we are uninterested. This fundamentally distorts how we interact with peers. While some enjoy this, we see it as disadvantageous, and ask to opt-out of this "filter bubble". –  Facebook made yet another change to its newsfeed. And the sites 750 million users didnt "like" it. Not one little bit. In a general howl of Internet, Facebooks user base appeared to rise up in fury over another massive site change. "NOOOO!" user Fiona posted in reply to the official announcement. "This is total garbage". "This makes me want to erase the Internet and just start over," griped Eric.Workshop on Social Recommender Systems UMAP-2012 Montreal 9
  10. 10. What are items of interest?•  Works that address this issue: –  Gilbert, E., Karahalios, K.: Predicting Tie Strength with Social Media, CHI-2009 –  Wu, A., DiMicco, J.M., Millen, D.R.: Detecting Professional versus Personal Closeness using an Enterprise Social Network Site, CHI-2010 –  Paek, T., Gamon, M., Counts, S., Chickering D.M., Dhesi, A.: Predicting the Importance of Newsfeed Posts and Social Network Friends, AAAI-2010 –  Freyne, J., Berkovsky, S., Smith, G.: Social Networking Feeds: Recommending Items of Interest, RecSys-2010 –  Guy, I., Ronen, I., Raviv, A.: Personalized Activity Streams: Sifting through the River of News, RecSys-2011 –  Berkovsky, S., Freyne, J., Smith, G.,: Personalized Network Updates: Increasing Social Interactions and Contributions in Social Networks, UMAP-2012Workshop on Social Recommender Systems UMAP-2012 Montreal 10
  11. 11. [Gilbert and Karaholios, 2009]•  Predict the strength of ties –  Facebook platform•  70 features from 7 tie strength dimensions –  Intensity: amount of communication exchanged –  Intimacy: use of intimacy and familiarity language –  Duration: period since establishing the ties –  Reciprocal: resources, apps, information shared –  Structural: groups, networks, interests shared –  Emotional: gifts, congratulations exchanged –  Social distance: religion, education, political differenceWorkshop on Social Recommender Systems UMAP-2012 Montreal 11
  12. 12. [Gilbert and Karaholios, 2009]•  Tie strength = linear combination of the 70 features –  Regression model to determine the predictive correlation of categories and individual features –  Binary classification •  weak | strong ties•  Ground truth –  35 participants –  More than 2000 explicit user tie strength judgmentsWorkshop on Social Recommender Systems UMAP-2012 Montreal 12
  13. 13. [Gilbert and Karaholios, 2009]•  Categories •  Features•  Predictive error = 10%Workshop on Social Recommender Systems UMAP-2012 Montreal 13
  14. 14. [Wu et al., 2010]•  Predict professional and personal closeness –  Corporate intranet social network•  60 features from 5 categories –  Subject: activity of the subject user –  Target: activity of the target user –  Direct: intensity of direct interaction between the subject and target user –  Indirect: intensity of indirect (through common friends) interaction between the subject and target user –  Corporate: distance in the organizational structure between the subject and target userWorkshop on Social Recommender Systems UMAP-2012 Montreal 14
  15. 15. [Wu et al., 2010]•  Professional and personal closeness = linear combination of the 60 variables –  Regression model to determine the predictive correlation of categories and individual features –  Continuous closeness prediction•  Ground truth –  196 participants –  4009 pairs of explicit closeness scores (both professional and personal) judgments •  correlation=0.48 between professional and personal scoresWorkshop on Social Recommender Systems UMAP-2012 Montreal 15
  16. 16. [Wu et al., 2010]•  Predictive categories for each closeness•  Predictive error –  18% error for professional closeness –  22% error for personal closenessWorkshop on Social Recommender Systems UMAP-2012 Montreal 16
  17. 17. [Paek et al., 2010]•  Predict the importance of news feed posts and interest in activities of others –  Facebook platform•  Linear combination of available content of Facebook accounts –  Social media: metadata and statistics of posts and users –  Content: textual content •  tf x idf, n-grams –  Background: static information about location, religion, education, interests, etcWorkshop on Social Recommender Systems UMAP-2012 Montreal 17
  18. 18. [Paek et al., 2010]•  Linear SVM classifier using Sequential Minimal Optimization•  Ground truth –  24 participants –  3241 explicit feed item ratings –  4238 explicit user ratings•  Findings –  34 out of 50 selected features relate to content •  Remaining 16 relate to social media –  Binary relevance classifiers trained •  Feed item classification accuracy = 0.64 •  User classification accuracy = 0.85Workshop on Social Recommender Systems UMAP-2012 Montreal 18
  19. 19. [Freyne et al., 2010]•  Recommend news feed items –  Corporate intranet social network•  Implicit feed item relevance judgment from clicks –  Action: frequency of performing an action or viewing content produced by the action –  User: frequency of interacting with a user or viewing content contributed by the user –  Temporal dimension •  Long-term – lifetime of the social network •  Short-term – last month onlyWorkshop on Social Recommender Systems UMAP-2012 Montreal 19
  20. 20. [Freyne et al., 2010]•  Ground truth –  1800 feed clicks •  Actual feed reconstructed •  Fictitious feeds referring to different scoring models constructed•  Accuracy of models = position of clicked items in the fictitious feed•  Findings –  Viewing actions/users predicts more accurately than performing actions or interacting with users •  Long-term model superior to short-term –  Combined model is most accurateWorkshop on Social Recommender Systems UMAP-2012 Montreal 20
  21. 21. [Guy et al., 2011]•  Recommend news feed items –  Corporate intranet social network•  User profiling: explicit selection of interesting –  People: direct and indirect relations –  Terms: contributed text, tags –  Places: used resources •  Blog, wiki, files, etc•  Recommendation of news items containing interesting people/terms/places (individually)•  Explicit evaluation of the selected items –  Ternary: not_interesting | interesting | very_interestingWorkshop on Social Recommender Systems UMAP-2012 Montreal 21
  22. 22. [Guy et al., 2011]•  Ground truth –  126 users –  Up to 10 people/terms/places of interest selected –  5 feed items recommended for each category of interest•  Feed item interest scoresWorkshop on Social Recommender Systems UMAP-2012 Montreal 22
  23. 23. [Berkovsky et al., 2012]•  Re-rank news feed items –  Health related social network•  Linear combination of implicit user and action scores from clicks –  User: feature categories of [Wu et al., 2010] weighted according to model of [Gilbert and Karaholios, 2009] –  Actions: normalized frequency of performing an action –  Ranking of feed items according to predicted score•  Ground truth –  530 feed clicks •  Actual feed reconstructedWorkshop on Social Recommender Systems UMAP-2012 Montreal 23
  24. 24. [Berkovsky et al., 2012]•  Rank of clicked feed items 35% personalized 30% non-personalized 25% percent of clicks 20% 15% 10% 5% 0% 1 2 3 4 5 6 7 8 9 10 rank•  Findings –  Increased contribution of user-generated content •  Forum, blog, and walls posts –  Highlights activities of online friendsWorkshop on Social Recommender Systems UMAP-2012 Montreal 24
  25. 25. Synthesis Paper Social Model Individual/ Explicit/ Evaluation network features combined implicit[Gilbert and Facebook User only (7 Individual Explicit and OfflineKaraholios] categories) implicit[Wu et al] Corporate User only (5 Individual Implicit Offline intranet categories)[Paek et al] Facebook Social media, Combined Implicit Offline content, background[Freyne et Corporate User, action, Combined Implicit Offlineal] intranet time simulated[Guy et al] Corporate User, content, Individual Explicit Offline intranet resource[Berkovsky eHealth User, action Combined Implicit Onlineet al] Workshop on Social Recommender Systems UMAP-2012 Montreal 25
  26. 26. Where to next?•  Feed item scoring –  Process and interpret content beyond text –  Combine interest scores for users, activities, content, sources, etc –  Personalized category/feature/item scoring•  Presentation –  Aggregation of feed items –  Visualization of salient activities –  Trust/privacy implications•  Generalization –  Develop models suitable to multiple social networks –  Communication across multiple social networks •  Offline communicationWorkshop on Social Recommender Systems UMAP-2012 Montreal 26
  27. 27. Shameless Self-Promotion8th International Conference on Persuasive Technology Sydney AU, 3-5 April 2013, http://pt2013.csiro.au Submissions: 15 November, 2012 Scope: technologies that affect users and behaviour Mobile and ubiquitous persuasion, persuasion and social media, personalized persuasion, persuasion in smart environments, learning and persuasion, persuasive UI, persuasion through entertainment, persuasion for health and sustainability, ...Workshop on Social Recommender Systems UMAP-2012 Montreal 27 !
  28. 28. Questions? Discussion? Thank You!Workshop on Social Recommender Systems UMAP-2012 Montreal
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