A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
On the real-time web as a source of recommendation knowledge
Proceedings of the fourth ACM conference on Recommender systems
Terms of a feather: content-based news recommendation and discovery using twitter
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Dynamic adaptation of numerical attributes in a user profile
Applied Intelligence
Automatic preference learning on numeric and multi-valued categorical attributes
Knowledge-Based Systems
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In this work we propose that the high volumes of data on real-time networks like Twitter can be harnessed as a useful source of recommendation knowledge. We describe Buzzer, a news recommendation system that is capable of adapting to the conversations that are taking place on Twitter. Buzzer uses a content-based approach to ranking RSS news stories by mining trending terms from both the public Twitter timeline and from the timeline of tweets generated by a user's own social graph (friends and followers). We also describe the result of a live-user trial which demonstrates how these ranking strategies can add value to conventional RSS ranking techniques, which are largely recency-based.