Abstract
Searching information by using search engines and browsers is a tedious task for users. Navigational and informational search tasks are complicated by the fact that web servers always provide complete web pages and do not tailor their content to the user's current information need. In this paper, we present a proposal for the application of context-aware recommendation techniques to simulate ...
Abstract
Searching information by using search engines and browsers is a tedious task for users. Navigational and informational search tasks are complicated by the fact that web servers always provide complete web pages and do not tailor their content to the user's current information need. In this paper, we present a proposal for the application of context-aware recommendation techniques to simulate human decision making when selecting elements of content to be included in an answer to an information need. As a first step towards live generation of content, we present results on our experimental study to capture decision criteria for this selection problem that web users apply in choosing content. These preferences could then later be formalized in terms of a knowledge-based context-aware and personalized model for recommending content during information search.
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