Are people biased in their use of search engines?
Communications of the ACM - Alternate reality gaming
Motivations for social networking at work
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting professional versus personal closeness using an enterprise social network site
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Selecting items of relevance in social network feeds
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Personalized techniques for lifestyle change
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Personalized activity streams: sifting through the "river of news"
Proceedings of the fifth ACM conference on Recommender systems
Personalized network updates: increasing social interactions and contributions in social networks
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Swimming against the streamz: search and analytics over the enterprise activity stream
Proceedings of the 21st ACM international conference on Information and knowledge management
Automatic on-device filtering of social networking feeds
Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design
Network activity feed: finding needles in a haystack
Proceedings of the 4th International Workshop on Modeling Social Media
Recommendation for online social feeds by exploiting user response behavior
Proceedings of the 22nd international conference on World Wide Web companion
Using link semantics to recommend collaborations in academic social networks
Proceedings of the 22nd international conference on World Wide Web companion
A novel mobile device user interface with integrated social networking services
International Journal of Human-Computer Studies
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The success of social media has resulted in an information overload problem, where users are faced with hundreds of new contributions, edits and communications at every visit. A prime example of this in social networks is the news or activity feeds, where the actions (friending, commenting, photo sharing, etc) of friends on the network are presented to users in order to inform them of the network activity. In this work we endeavour to reduce the burden on individuals of identifying interesting updates in social network news feeds by automatically identifying and recommending relevant items to individuals where item relevance is based on the observed interactions of the individual with the social network. The results of our offline study show that combining short term interest models, exploiting previous viewing behavior of users, and long-term models, exploiting previous viewing of network actions, was the best predictor of feed item relevance.