GAIN: Analysis of Implicit Feedback on Semantically Annotated Content

GAIN: Analysis of Implicit Feedback on Semantically Annotated ContentDownload PDF

Author: KUCHAŘ, Jaroslav, KLIEGR, Tomáš

In Proceedings of The 7th Workshop on Intelligent and Knowledge Oriented Technologies (WIKT 2012) was held in Smolenice, Slovakia from 22–23 November 2012. Bratislava : Nakladateľstvo STU, 2012, p. 75–78. ISBN 978-80-227-3812-5.

The trend in application development is to provide a personalized interface. The availability of the user preference level associated with user actions is the key for the personalization process. This paper describes a “work-in-progress” framework for deriving user preference from actions performed on semantically annotated objects – be it web pages or TV news. Preference level is computed using supervised learning with genetic programming from implicit feedback, which might be time on page for the web domain, or the user engagement level for the TV domain. We provide tool called GAIN (General Analytics INterceptor) covering the whole approach at

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