Short and tweet: experiments on recommending content from information streams

More and more web users keep up with newest information through information streams such as the popular microblogging 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. Author Keywords Information stream, recommender system, topic modeling, social filtering. ACM Classification Keywords H.5.3: Group and Organization Interfaces. General Terms Algorithms...

• Type: Conference
• Year: 2010
• Where: CHI
• Authors: Jilin Chen, Rowan Nairn, Les Nelson, Michael Bernstein, Ed Chi
• Keywords: CHI 2010, Human Computer Interaction, Information Stream, Microblogging Website Twitter, Real Twitter Users