Domain-Specific Modeling of User Knowledge in Informational Search Sessions

Abstract

Users frequently search on the Web to fulfill information needs with learning intent. In this context, usefulness of the search results depends strongly on the knowledge state of the user. In order to satisfy learning needs effectively, it is necessary to take users' knowledge gain and knowledge state within learning-oriented Web search sessions into account. Previous works studied the use of supervised models to predict a user's knowledge gain and knowledge state. However, the impact of knowledge domains of the search topics on a user's learning process have not been adequately explored. In this paper, we suggest domain detection techniques for search sessions and build domain-specific knowledge prediction models accordingly. Experimental evaluation results demonstrate that our approach outperforms the state-of-the-art baseline.