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Searching information on web pages is a tedious task forusers as web servers provide complete web pages and do not tailor theircontent to the user's current information need. This leaves an enormous amountof workload for the user and influences his emotional attitude towardsthe whole task even if a search engine has filtered pages that are relevant toa user query. In this paper, we propose an approach to adapt the response toqueries to user preferences for his reading experience in order to leverage theproblem of information overload. With these preferences, it is possible toselect the most preferred content from a web page. In our view, thepreferences are a quantitative way to express conversational maxims. We presentour experimental approach to learn these preferences from annotated browsingsessions and introduce a decision strategy for the selection of content on thebasis of the learned preferences.

What Readers want to Experience - An Approach to Quantify Conversational Maxims with Preferences for Reading Behaviour