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. © 2010 ACM.
CITATION STYLE
Chen, J., Nairn, R., Nelson, L., Bernstein, M., & Chi, E. (2010). Short and tweet: Experiments on recommending content from information streams. In Conference on Human Factors in Computing Systems - Proceedings (Vol. 2, pp. 1185–1194). https://doi.org/10.1145/1753326.1753503
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