Towards a Tag-Based User Model: How Can User Model Benefit from Tags?
UM '07 Proceedings of the 11th international conference on User Modeling
Recommending twitter users to follow using content and collaborative filtering approaches
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
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Signal-based user recommendation on twitter
Proceedings of the 22nd international conference on World Wide Web companion
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In this paper we describe an initial attempt to build multi-faceted user models from raw Twitter data. The key contribution is to describe a technique for categorising users and their social ties according to a collection of curated topical categories and in this way resolve much of the preference noise that is inherent within user conversations. We go on to analyse and evaluate this approach on a data set of over 240,000 Twitter users and discuss the applications of these novel user models.