Identifying the User's Query Intent in Web Search: an Unsupervised Approach

Web Information Retrieval systems need to increase the user satisfaction while improving the quality of the results. The current trend in this direction focus on representing the information needs of the user along with the query. This representation need to be automatic and transparent to the furthest extent possible. In this work the focus is on identifying and understanding the user intent: What motivated the user to perform a search on the web? To this end, we apply machine learning models not requiring more information than the one provided by the very needs of the users, which in this work are represented by their queries. The knowledge and interpretation of this invaluable information can help search engines to obtain resources, especially relevant to users, and thus improve user satisfaction. By means of unsupervised learning techniques, which have been selected according to the context of the problem being solved, throughout this research work, we show that is not only possible to identify the user's intents but that this process can be done automatically.