Interpreting user actions to better understand their needs provides an important tool for improving information access services. In the context of organic Web search, considerable effort has been made to model user behavior and infer query intent, with the goal of improving the overall user experience. Much less work has been done in the area of sponsored search, i.e., with respect to the advertisement links (ads) displayed on search result pages by many commercial search engines. This thesis develops and evaluates new models and methods required to interpret user browsing and click behavior and understand query intent in this very different context. The concern of the initial part of the thesis is on extending the query categories for commercial search and on inferring query intent, with a focus on two major tasks: i) enriching queries with contextual information obtained from search result pages returned for these queries, and ii) developing relatively simple methods for the reliable labeling of training data via crowdsourcing. A central idea of this thesis work is to study the impact of contextual factors (including query intent, ad placement, and page structure) on user behavior. Later, this information is incorporated into probabilistic models to evaluate the quality of advertisement links within the context that they are displayed in their history of appearance. In order to account for these factors, a number of query and location biases are proposed and formulated into a group of browsing and click models. To explore user intent and behavior and to evaluate the performance of the proposed models and methods, logs of query and click information provided for research purposes are used. Overall, query intent is found to have substantial impact on predictions of user click behavior in sponsored search. Predictions are further improved by considering ads in the context of the other ads displayed on a result page. The parameters of the browsing and click models are learned using an expectation maximization technique applied to click signals recorded in the logs. The initial motivation of the user to browse the ad list and their browsing persistence are found to be related to query intent and browsing/click behavior. Accommodating these biases along with the location bias in user models appear as effective contextual signals, improving the performance of the existing models.

Interpreting user actions to better understand their needs provides an important tool for improving information access services. In the context of organic Web search, considerable effort has been made to model user behavior and infer query intent, with the goal of improving the overall user experience. Much less work has been done in the area of sponsored search, i.e., with respect to the advertisement links (ads) displayed on search result pages by many commercial search engines. This thesis develops and evaluates new models and methods required to interpret user browsing and click behavior and understand query intent in this very different context. The concern of the initial part of the thesis is on extending the query categories for commercial search and on inferring query intent, with a focus on two major tasks: i) enriching queries with contextual information obtained from search result pages returned for these queries, and ii) developing relatively simple methods for the reliable labeling of training data via crowdsourcing. A central idea of this thesis work is to study the impact of contextual factors (including query intent, ad placement, and page structure) on user behavior. Later, this information is incorporated into probabilistic models to evaluate the quality of advertisement links within the context that they are displayed in their history of appearance. In order to account for these factors, a number of query and location biases are proposed and formulated into a group of browsing and click models. To explore user intent and behavior and to evaluate the performance of the proposed models and methods, logs of query and click information provided for research purposes are used. Overall, query intent is found to have substantial impact on predictions of user click behavior in sponsored search. Predictions are further improved by considering ads in the context of the other ads displayed on a result page. The parameters of the browsing and click models are learned using an expectation maximization technique applied to click signals recorded in the logs. The initial motivation of the user to browse the ad list and their browsing persistence are found to be related to query intent and browsing/click behavior. Accommodating these biases along with the location bias in user models appear as effective contextual signals, improving the performance of the existing models.

This doctoral 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, we show that is not only possible to identify the user’s intents, but that this process can be conducted automatically. The research conducted in this thesis 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. Furthermore, from this last proposal, we have configured a framework for the on–line identification of the user’s query intent. Overall, the results from this research have shown to be effective for the problem of identifying user’s query intent. / Este trabajo doctoral se enfoca en identificar y entender las intenciones que motivan a los usuarios a realizar búsquedas en la Web a través de la aplicación de métodos de aprendizaje automático que no requieren datos adicionales más que las necesidades de información de los mismos usuarios, representadas a través de sus consultas. El conocimiento y la interpretación de esta información, de valor incalculable, puede ayudar a los sistemas de búsqueda Web a encontrar recursos particularmente relevantes y así mejorar la satisfacción de sus usuarios. A través del uso de técnicas de aprendizaje no supervisado, las cuales han sido seleccionadas dependiendo del contexto del problema a solucionar, y cuyos resultados han demostrado ser efectivos para cada uno de los problemas planteados, a lo largo de este trabajo se muestra que no solo es posible identificar las intenciones de los usuarios, sino que este es un proceso que se puede llevar a cabo de manera automática. La investigación desarrollada en esta tesis ha implicado un proceso evolutivo, el cual inicia con el análisis de la clasificación manual de diferentes conjuntos de consultas que usuarios reales han sometido a un motor de búsqueda. El trabajo pasa a través de la proposición de una nueva clasificación de las intenciones de consulta de usuarios, y el uso de diferentes técnicas de aprendizaje no supervisado para identificar dichas intenciones, llegando hasta establecer que éste no es un problema unidimensional, sino que debería ser considerado como un problema de múltiples dimensiones, donde cada una de dichas dimensiones, o facetas, contribuye a clarificar y establecer cuál es la intención del usuario. A partir de este último trabajo, hemos creado un modelo para la identificar la intención del usuario en un escenario on–line.