What Readers Want to Experience: An Approach to Quantify Conversational Maxims with Preferences for Reading Behaviour

Searching information on web pages is a tedious task for users as web servers provide complete web pages and do not tailor their content to the user's current information need. This leaves an enormous amount of workload for the user and influences his emotional attitude towards the whole task even if a search engine has filtered pages that are relevant to a user query. In this paper, we propose an approach to adapt the response to queries to user preferences for his reading experience in order to leverage the problem of information overload. With these preferences, it is possible to select the most preferred content from a web page. In our view, the preferences are a quantitative way to express conversational maxims. We present our experimental approach to learn these preferences from annotated browsing sessions and introduce a decision strategy for the selection of content on the basis of the learned preferences