Recommending content from social information streams

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People increasingly keep up with the "newest news" through information streams (such as Twitter). …

People increasingly keep up with the "newest news" through information streams (such as Twitter).

To alleviate information overload and better direct user attention, we explored dimensions for designing a recommender system that selects promising subsets of content for consideration, models user topic interest, and leverages social interaction processes.

The best performing algorithm -- implemented as a prototype web-based tool -- improved the percentage of interesting content to 72% (from a baseline of 33%). The competencies and results of this work can be generalized to other enterprise and consumer information streams.

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  • 1. Short and Tweet Experiments on Recommending Content from Information Streams Jilin Chen, University of Minnesota Rowan Nairn, Palo Alto Research Center Les Nelson, Palo Alto Research Center Ed H. Chi, Palo Alto Research Center Michael Bernstein, MIT CSAIL 28 April 2010
  • 2. Social Information Streams
  • 3.  
  • 4. Follower and Followee follows Follower Followees
  • 5. Two Problems
    • The Filtering Problem:
      • “ I get 1,000+ items in my stream daily but only have time to read 10 of them. Which ones should I read?”
    • The Discovery Problem:
      • “ There are millions of URLs posted daily on Twitter. Am I missing something important there outside my own Twitter stream?”
  • 6. Existing Solutions
    • Intelligent Filters
      • The Facebook Top News feed
      • Third-party RSS filters
    • Trend Detection Aggregators
      • The Twitter Trending Topics
      • Third-party Twitter Aggregators
  • 7. Our Solution: A Recommender
    • Zerozero88.com
      • Twitter as the platform
      • URLs as the items to recommend
      • Solves both problems
  • 8.
    • Recom m endation Engine
    • Multiply scores
    • Rank URLs using multiplied scores
    • Recommend highest ranked URLs
    URL Sources Topic Relevance Scores Social Network Scores User Topic Profiles Local Social Network
  • 9. URL Sources
    • Considering all URLs was impossible
    • FoF: URLs from followee-of-followees
    • Popular: URLs that are popular across whole Twitter
    Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular
  • 10.
    • Recom m endation Engine
    • Multiply scores
    • Rank URLs using multiplied scores
    • Recommend highest ranked URLs
    URL Sources Topic Relevance Scores Social Network Scores User Topic Profiles Local Social Network
  • 11. Topic Relevance Scores
    • Topic Profile of URLs
      • Use term vectors as profiles
      • Built from tweets that have mentioned the URL
      • Allows profiling of URLs pointing to non-textual content, e.g. images and videos
  • 12. Topic Profile of Users
    • Self-Topic: content profile based on what I post
    • Followee-Topic: content profile based on what my followees post
    • None, for comparison purpose
    Component Possib le Design Choices Topic Relevance Scores Self-Topic Followee-Topic None
  • 13.
    • Recom m endation Engine
    • Multiply scores
    • Rank URLs using multiplied scores
    • Recommend highest ranked URLs
    URL Sources Topic Relevance Scores Social Network Scores User Topic Profiles Local Social Network
  • 14. Social Network Scores
    • “ Popular Vote” in among my followees-of-followees
      • People “vote” a URL by tweeting it
      • Votes are weighted using social network structure
      • URLs with more votes in total are assigned higher score
    • None, for comparison purpose
    Component Possib le Design Choices Social Network Scores Social Voting None
  • 15. Possible Recommender Designs
    • 2 ( URL source ) x 3 (topic score ) x 2 (social score ) = 12 possible algorithm designs in total
    • Random selection if for both scores we chose None
    • Recom m endation Engine
    • Multiply scores
    • Rank URLs using multiplied scores
    • Recommend highest ranked URLs
    Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Scores Self-Topic Followee-Topic None Social Network Scores Social Voting None
  • 16. Field Study
    • Platform
      • Zerozero88.com
      • Publicized as a personalized news service
    • Recruiting
      • Recruited through word-of-mouth on Twitter
      • No Payment
      • Requires at least 20 followees and 50 tweets
    • Participants
      • 44 qualified Twitter users in 3-week period
  • 17. Field Study
    • Within-subject design
    • Each subject evaluated 5 URL recommendations from each of the 12 algorithms
      • Present 60 items in Random Order, and ask for binary rating
      • 60 x 44 = 2640 ratings in total
  • 18. Analysis Approach
    • Logistic Regression
      • Better p redict s binary output s, i.e. interest ratings
      • Compares design choices using odds-ratios
    • Generalized Linear Model
      • Handles data c orrelat ions
  • 19. Summary of Results Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Scores Self-Topic Followee-Topic None Social Network Scores Social Voting None FoF ? Popular 125% 100% (beta=0.22, Z=1.78, p=.08)
  • 20. Summary of Results (beta=0.58, Z=4.95, p<.001) (beta=0.27, Z=2.48, p=.01) Self-Topic > Followee-Topic > None 179% 131% 100% Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Scores Self-Topic Followee-Topic None Social Network Scores Social Voting None
  • 21. Summary of Results (beta=1.02, Z=6.53, p<.001) Voting > None 277% 100% Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Scores Self-Topic Followee-Topic None Social Network Scores Social Voting None
  • 22. Summary of Results
    • Diminishing return when combining the two approaches.
      • Social voting is the biggest contributor by itself.
      • Self-Topic adds 22% on top;
      • Followee-Topic adds less than 10%.
    Component Possib le Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Scores Self-Topic Followee-Topic None Social Network Scores Social Voting None
  • 23. Summary of Results Best Performing Social Vot e Only Popular URLs FoF URLs
  • 24. Generalizability
    • What zerozero88 requires:
      • People produce and consume text pieces over time
      • People explicitly engage in social interactions
      • The items to recommend can be naturally associated with people and text pieces
    • Therefore it can also be adapted to…
      • Recommend photos on Facebook
      • Recommend news stories on Google Reader
      • Recommend work items in open source projects if they use RSS to track items
      • Recommend emails in enterprise email systems
  • 25. Some Take-Aways
    • Recommenders can effectively extract interesting content from social information streams.
    • Ranking using topic relevance and social network are both effective and complementary to each other.
    • Our approach is fairly general; however, tailoring may still be necessary when applied to other platforms.
    • And…
  • 26. Algorithms Differ Not Only in Accuracy!
    • Relevance vs. Serendipity in recommendation algorithms
    • From a subject in the pilot interview of zerozero88:
      • “ There is a tension between the discovery and the affirming aspect of things. I am getting tweets about things that I am already interested in. Something I crave … , is an element of surprise or whimsy . .. . I am getting a lot of things I am interested in, but that is not necessarily a good thing for me personally ”
    • Suggests personalized choices of recommendation algorithms
  • 27. For more information, please contact:
    • Ed Chi, Principal Scientist
    • [email_address]
    • Lawrence Lee, Director of Business Development
    • Lawrence.Lee@parc.com
    • To subscribe to the PARC Innovations Update
    • e-newsletter or blog, or to follow us on Twitter, go to http://www.parc.com/about/subscribe.html