The document discusses social recommender systems and summarizes several research papers on predicting user interests in social networks. It begins by outlining the problem of information overload on social platforms. It then summarizes key findings from papers that used social media data and machine learning models to predict tie strength, closeness between users, importance of newsfeed posts and interest in other users. The document concludes by discussing open challenges and directions for future work in developing personalized social recommender systems.
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 stream
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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?
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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]
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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.
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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.
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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 site's 750 million users didn't "like" it. Not one
little bit. In a general howl of Internet, Facebook's 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.
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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-2012
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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 difference
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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 judgments
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13. [Gilbert and Karaholios, 2009]
• Categories • Features
• Predictive error = 10%
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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 user
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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 scores
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16. [Wu et al., 2010]
• Predictive categories for each closeness
• Predictive error
– 18% error for professional closeness
– 22% error for personal closeness
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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, etc
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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 = 0.85
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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 only
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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 accurate
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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_interesting
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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 scores
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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 reconstructed
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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 friends
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25. Synthesis
Paper Social Model Individual/ Explicit/ Evaluation
network features combined implicit
[Gilbert and Facebook User only (7 Individual Explicit and Offline
Karaholios] 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 Offline
al] intranet time simulated
[Guy et al] Corporate User, content, Individual Explicit Offline
intranet resource
[Berkovsky eHealth User, action Combined Implicit Online
et al]
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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 communication
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27. Shameless Self-Promotion
8th 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. Questions?
Discussion?
Thank You!
Workshop on Social Recommender Systems UMAP-2012 Montreal