Predicting users’ behavior using mouse movement information: an information foraging theory perspective

The prediction of users’ behavior is essential for keeping useful information on the web. Previous studies have used mouse cursor information in web usability evaluation and designing user-oriented search interfaces. However, we know fairly to a small extent pertaining to user behavior, specifically clicking and navigating behavior, for prolonged search session illustrating sophisticated search norms. In this study, we perform extensive analysis on a mouse movement activities dataset to capture every users’ movement pattern using the effects of information foraging theory (IFT). The mouse cursor movement information dataset includes the timing and positioning information of mouse cursors collected from several users in different sessions. The tasks vary in two dimensions: (1) to determine the interactive elements (i.e., information episodes) of user interaction with the site; (2) adopt these findings to predict users’ behavior by exploiting the LSTM model. Our model is developed to find the main patterns of the user’s movement on the site and simulate the behavior of users’ mouse movement on any website. We validate our approach on a mouse movement dataset with a rich collection of time and position information of mouse pointers in which searchers and websites are annotated by web foragers and information patches, respectively. Our evaluation shows that the proposed IFT-based effects provide an LSTM model a more accurate interpretative exposition of all the patterns in the movement of the users’ mouse cursors across the screen

Effect of reward contingencies on multiple-target visual search

It has long been known that human beings’ search behaviour is influenced by different mechanisms of control of attention: we can voluntarily pay attention, according to the context-specific goals, or we can involuntarily direct it, guided by the physical conspicuity of perceptual objects. Recent evidence suggests that pairing target stimuli with reward can modulate the way in which we voluntarily deploy our attention. In this thesis, the explored line of research focuses on the effects of reward, specifically a monetary reward: neutral stimuli are imbued with value via associative learning, through a training phase. This work aims to investigate if these stimuli will be able to capture attention in a subsequent foraging task. This mechanism, known as value-driven attentional capture, has never been investigated in a foraging context, but only in a classical visual search one: will it be able to influence the search behaviour when the targets are multiple

Interactive analogical retrieval: practice, theory and technology

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.PhDCommittee Chair: Goel, Ashok; Committee Member: Kolodner, Janet; Committee Member: Maher, Mary Lou; Committee Member: Nersessian, Nancy; Committee Member: Yen, Jeannett

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Going Beyond Relevance: Role of effort in Information Retrieval

The primary focus of Information Retrieval (IR) systems has been to optimize for Relevance. Existing approaches to rank documents or evaluate IR systems does not account for “user effort”. Currently, judges only determine whether the information provided in a given document would satisfy the underlying information need in a query. The current mechanism of obtaining relevance judgments does not account for time and effort that an end user must put forth to consume its content. While a judge may spend a lot of time assessing a document, an impatient user may not devote the same amount of time and effort to consume its content. This problem is exacerbated on smaller devices like mobile. While on mobile or tablets, with limited interaction, users may not put in too much effort in finding information. This thesis characterizes and incorporates effort in Information Retrieval. Comparison of explicit and implicit relevance judgments across several datasets reveals that certain documents are marked relevant by the judges but are of low utility to an end user. Experiments indicate that document-level effort features can reliably predict the mismatch between dwell time and judging time of documents. Explicit and preference-based judgments were collected to determine which factors associated with effort agreed the most with user satisfaction. The ability to locate relevant information or findability was found to be in highest agreement with preference judgments. Findability judgments were also gathered to study the association of different annotator, query or document related properties with effort judgments. We also investigate how can existing systems be optimized for relevance and effort. Finally, we investigate the role of effort on smaller devices with the help of cost-benefit models