Short and tweet: experiments on recommending content from information streams

  • Authors:

  • Jilin Chen;Rowan Nairn;Les Nelson;Michael Bernstein;Ed Chi

  • Affiliations:

  • University of Minnesota, Minneapolis, MN, USA;Palo Alto Research Center, Palo Alto, CA, USA;Palo Alto Research Center, Palo Alto, CA, USA;Massachusetts Institute of Technology, Cambridge, MA, USA;Palo Alto Research Center, Palo Alto, CA, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2010

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Abstract

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.