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.

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

4. Follower and Followee follows Follower Followees

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

23. Summary of Results Best Performing Social Vot e Only Popular URLs FoF URLs