Fecha: 5 octubre 2011
Lugar de celebración: Sala 2.24. Facultad de Psicología, UNED
This work focuses on identifying and understanding the intents that motivate a user to perform a search on the Web. To this end, we apply machine learning models that do not require 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 their satisfaction.
By means of unsupervised learning techniques, which have been selected according to the context of the problem being solved, throughout this work we show that is not only possible to identify the user's intents, but that this process can be conducted automatically.
The research conducted has involved an evolutionary process that starts from the manual analysis of different sets of real user queries from a search engine. The work passes through the proposition of a new classification of user's query intents; the application of different unsupervised learning techniques to identify those intents; up to determine that the user's intents, rather than being considered as an uni-dimensional problem, should be conceived as a composition of several aspects, or dimensions (i.e., as a multi-dimensional problem), that contribute to clarify and to establish what the user's intents are. The results from this research have shown to be effective for the problem of identifying user's query intent.