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.

Search engines are in a constant need for improvements as the rapid growth of information is affecting the search engines ability to return documents with high relevance. Search results are being lost in between pages and the search algorithms are being exploited to gain a higher ranking on the documents. This study attempts to minimize those two issues, as well as increasing the relevancy of search results by usage of clickthrough data to add another layer of weighting the search results. Results from the evaluation indicate that clickthrough data in fact can be used to gain more relevant search results.

The paper examines Meta ads in the adult education field. The ads are selected based onfour main criteria, ads with creatives that show people in the profession of the intendededucation, ads that the audience can quickly identify as an educational program, creativeswith a high signal-to-noise ratio and creatives with only one point of focus in the pictures.The ads are analyzed through a semiotic analysis and then the subject of a survey todetermine how they score in regards to informativeness, entertainment, irritation and speedof processing. The paper finds a connection between ads with a high CTR (Clickthroughrate) and high scores in the survey in “informative” and “fast to process” ads. The mainlimitation of the paper is the low amount of partakers in the survey, coupled with the methodof scoring ads based on a survey instead of in their natural habitat, not accounting forbanner blindness and other phenomena related to social media and the web.

In this thesis, we aim at improving the search result quality by utilizing the search intelligence (history of searches) available in the form of click-through data. We address two key issues, namely 1) relevance feedback extraction and fusion, and 2) deciphering search query intentions. Relevance Feedback Extraction and Fusion: The existing search engines depend heavily on the web linkage structure in the form of hyperlinks to determine the relevance and importance of the documents. But these are collective judgments given by the page authors and hence, prone to collaborated spamming. To overcome the spamming attempts and language semantic issues, it is also important to incorporate the user feedback on the documents' relevance. Since users can be hardly motivated to give explicit/direct feedback on search quality, it becomes necessary to consider implicit feedback that can be collected from search engine logs. Though a number of implicit feedback measures have been proposed in the literature, we have not been able to identify studies that aggregate those feedbacks in a meaningful way to get a final ranking of documents. In this thesis, we first evaluate two implicit feedback measures namely 1) click sequence and 2) time spent on the document for their content uniqueness. We develop a mathematical programming model to collate the feedbacks collected from different sessions into a single ranking of documents. We use Kendall's τ rank correlation to determine the uniqueness of the information content present in the individual feedbacks. The experimental evaluation on top 30 select queries from an actual search log data confirms that these two measures are not in perfect agreement and hence, incremental information can potentially be derived from them. Next, we study the feedback fusion problem in which the user feedbacks from various sessions need to be combined meaningfully. Preference aggregation is a classical problem in economics and we study a variation of it where the rankers, i.e., the feedbacks, possess different expertise. We extend the generalized Mallows' model to model the feedback rankings given in user sessions. We propose a single stage and two stage aggregation framework to combine different feedbacks into one final ranking by taking their respective expertise into consideration. We show that the complexity of the parameter estimation problem is exponential in number of documents and queries. We develop two scalable heuristics namely, 1) a greedy algorithm, and 2) a weight based heuristic, that can closely approximate the solution. We also establish the goodness of fit of the model by testing it on actual log data through log-likelihood ratio test. As the independent evaluation of documents is not available, we conduct experiments on synthetic datasets devised appropriately to examine the various merits of the heuristics. The experimental results confirm the possibility of expertise oriented aggregation of feedbacks by producing orderings better than both the best ranker as well as equi-weight aggregator. Motivated with this result, we extend the aggregation framework to hold infinite rankings for the meta-search applications. The aggregation results on synthetic datasets are found to be ensuring the extension fruitful and scalable. Deciphering Search Query Intentions: The search engine often retrieves a huge list of documents based on their relevance scores for a given query. Such a presentation strategy may work if the submitted query is very specific, homogeneous and unambiguous. But many a times it so happen that the queries posed to the search engine are too short to be specific and hence ambiguous to identify clearly the exact information need, (eg. "jaguar"). These ambiguous and heterogeneous queries invite results from diverse topics. In such cases, the users may have to sift through the entire list to find their needed information and that could be a difficult task. Such a task can be simplified by organizing the search results under meaningful subtopics, which would help the users to directly move on to their topic of interest and ignore the rest. We develop a method to determine the various possible intentions of a given short generic and ambiguous query using information from the click-through data. We propose a two stage clustering framework to co-cluster the queries and documents into intentions that can readily be presented whenever it is demanded. For this problem, we adapt the spectral bipartite partitioning by extending it to automatically determine the number of clusters hidden in the log data. The algorithm has been tested on selected ambiguous queries and the results demonstrate the ability of the algorithm in distinguishing among the user intentions.