A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Content-based recommendation systems
The adaptive web
Dynamic adaptation of numerical attributes in a user profile
Applied Intelligence
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With the increasing growth of online communication tools, as well as consumption of topical and current information from the web, there is a growing difficulty for users to keep abreast of current, relevant and interesting material. The widespread online adoption of techniques such as recommender systems has come about due to their proven ability to reduce and personalise the constituents of the information explosion. The collective conversations found on such services as Twitter are playing an increasingly useful role in monitoring current and topical trends among a large set of culturally and geographically diverse users. In this paper, we describe the ongoing development of a system that harnesses real-time micro-blogging activity such as Twitter, as a basis for promoting and influencing personalized online news and blog content. The system provides a real-time way for users to engage with content that has been influenced by popular activity of both the global community, or their own friends. We also discuss some preliminary results based on a live user evaluation.