Personalized Search on the Query Side

Information retrieval systems such as web search engines are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack implicit user modeling and thus not adaptive to individual users, resulting in inherently non-optimal retrieval performance. The basic idea of this work is to make use of the implicit relevance feedback present in the user's interactions with search results in order to improve the web search ranking and automatically augment queries that better describe the user’s need. This is achieved using a client-side web search agent that resides locally on the user’s machine. The tendency towards a client-side approach provides privacy and ensures that all the user’s interactions are recorded.