query-page intention matching using clicked titles and snippets to boost search rankings
Users of text retrieval systems input only a few keywords or sometimes just one keyword to the systems even if they had complex information needs. Due to the lack of query keywords, it becomes hard to return relevant search results that satisfy the demands of each user. Because digital documents, in contrast to queries, are generally composed of many kinds of keywords, it is also difficult to estimate the main topic or grasp the inherent intentions of the documents. In this paper, we present techniques to represent users' search intentions and the intentions that digital documents can satisfy by making use of clicked titles and snippets acquired from a click log analysis. We then present a method to match these intentions to boost search result rankings. Through experiments that use click logs and indexes of a commercial search engine, we verified our method's capability of significantly improving search precision.
click logs analysis search result rankings representation of intention implicit relevance feedback