Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objects. To overcome this limitation, we investigated whether by analyzing a searcher's current processes we could forecast his likelihood of achieving a certain level of success with respect to search performance in the future. We propose a machine‐learning‐based method to dynamically evaluate and predict search performance several time‐steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted from expression of information need and coverage of information. For testing, we used log data collected from 4 user studies that included 216 users (96 individuals and 60 pairs). Our results show 80–90% accuracy in prediction depending on the number of time‐steps ahead. In effect, the work reported here provides a framework for evaluating search processes during exploratory search tasks and predicting search performance. Importantly, the proposed approach is based on user processes and is independent of any IR system.

Personalization of support for information seeking depends crucially on the information retrieval system's knowledge of the task that led the person to engage in information seeking. Users work during information search sessions to satisfy their task goals, and their activity is not random. To what degree are there patterns in the user activity during information search sessions? Do activity patterns reflect the user's situation as the user moves through the search task under the influence of his or her task goal? Do these patterns reflect aspects of different types of information-seeking tasks? Could such activity patterns identify contexts within which information seeking takes place? To investigate these questions, we model sequences of user behaviors in two independent user studies of information search sessions (N = 32 users, 128 sessions, and N = 40 users, 160 sessions). Two representations of user activity patterns are used. One is based on the sequences of page use; the other is based on a cognitive representation of information acquisition derived from eye movement patterns in service of the reading process. One of the user studies considered journalism work tasks; the other concerned background research in genomics using search tasks taken from the TREC Genomics Track. The search tasks differed in basic dimensions of complexity, specificity, and the type of information product (intellectual or factual) needed to achieve the overall task goal. The results show that similar patterns of user activity are observed at both the cognitive and page use levels. The activity patterns at both representation layers are able to distinguish between task types in similar ways and, to some degree, between tasks of different levels of difficulty. We explore relationships between the results and task difficulty and discuss the use of activity patterns to explore events within a search session. User activity patterns can be at least partially observed in server-side search logs. A focus on patterns of user activity sequences may contribute to the development of information systems that better personalize the user's search experience.

Can the activity patterns of page use during information search sessions discriminate between different types of information seeking tasks? We model sequences of interactions with search result and content pages during information search sessions. Two representations are created: the sequences of page use and a cognitive representation of page interactions. The cognitive representation is based on logged eye movement patterns of textual information acquisition via the reading process. Page sequence actions from task sessions (n=109) in a user study are analyzed. The study tasks differed from one another in basic dimensions of complexity, specificity,level, and the type of information product (intellectual or factual). The results show that differences in task types can be measured at both the level of observations of page type sequences and at the level of cognitive activity on the pages. We discuss the implications for personalization of search systems, measurement of task similarity and the development of user-centered information systems that can support the user's current and expected search intentions.

Analyzing people's Web search behavior has been a significant topic of interest in the Information Retrieval domain and search engine industry over the past decade. Research in this area has focused on improving search and retrieval capabilities leading to high demands and expectations of Web search users. Understanding and analyzing the Web search process when users are performing Web search tasks is a challenging problem due to many reasons such as subjectivity, dynamic nature, difficulty in measurement of success and difficulty in evaluation. I propose to analyze the users' Web search behavior in order to identify the strategies and tactics they use in fulfilling their task. In order to achieve this, I intend to use data mining and machine learning methods with an emphasis on time series analysis given that the user search process can be considered as a sequence of time related events.

In general, IR systems assist searchers by predicting or assuming what could be useful for their information needs by providing query suggestions or pseudo-relevance feedback. Most of these approaches are based on analyzing information objects (documents, queries) seen or used in the past and then proposing other related objects that may be relevant. Such approaches often ignore the underlying process of information seeking that guides how a searcher performs during information seeking episode, thus forgoing opportunities for making process-based recommendations. In order to address this, we propose a search process-based analysis of discovering different segments, which leads to analyzing different search action based features and evaluating the search performance for each stage. Further, we propose a query recommendation strategy to improve the search performance of each low performing user for each stage, which shows that the proposed overall model yields effective search performance improvements above 90% in most cases. This could lead to better recommendations and optimizations within each segment in order to enhance the overall search performance of a user.

Analyzing the changes in volatility is an important aspect in financial data analysis leading to effective estimation of risk and discovering underlying causes of such changes. While there is a rich literature in estimating implied and stochastic volatility in financial time series using traditional econometric methods, the application of machine learning methods such as sparse regression with temporal smoothness constraints is still in its infancy. In this paper, we propose a sparse, smooth regularized regression model to infer the volatility of the data while explicitly accounting for dependencies between different companies. Using real stock market data, we construct dynamic time varying graphs for different sectors of companies to further analyze how the volatility dependency between companies within sectors vary over time. We also show how our model captures the fluctuations in volatility over different economic conditions such as financial crisis periods. Further, based on these regression estimates we show how the proposed model assists in discovering useful correlations with external factors such as oil price, inflation, S&P500 index and also with various domestic trend indices.