Relevance Feedback using Query Logs

A search engine retrieves the documents based on the query submitted to it. However, incorporation of user modelling, by the inclusion of past information (like the previous queries submitted and the titles of the documents clicked) is expected to increase the accuracy of the search results. Especially, in the case of short term history, such history information is highly related with the current user query and can help in explaining the user information needs in a batter way. In order to do the same, we develop and experiment with some “history incorporation and term reweighting” techniques that incorporate the user history along with the current query. These techniques expand the current query by including terms from the history queries and the document titles, and reweight the terms. The results confirm that the incorporation of history along with the current query is considerably better in performance than the usage of only the current query. We compare the retrieval models against each other and also analyze the combinations of the retrieval models with the incorporation and reweighting techniques.