Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape

Web Augmentation as a Technique to Diminish User Interactions in Repetitive Tasks

The use of the World Wide Web has experienced extraordinary growth in the last decades. The Web has become the main source of information for millions of users. The number of websites offering content to users is countless. In order to personalise information according to their needs, users often have to visit multiple, unconnected pages. Users perform a number of actions to collect that information that requires concentration. If the number of Web resources is large, the activity becomes unpleasant. The problem increases when these tasks are performed frequently and repetitively. These tasks are time-consuming and lead users to experience frustration and disorientation during the activity, causing a loss of concentration that prolongs the activity over time. Web Augmentation combines different Web technologies to improve user experience on existing pages by adding content from different pages among other benefits. This article proposes Web Augmentation as a technique to reduce user interactions in repetitive tasks. To support the proposal, the paper introduces Excore, a browser extension for Web Augmentation that allows end-users to add content from different resources automatically. The article presents the benefits introduced by this approach as a response to the drawbacks experienced by users while performing their activities on the Web. The architecture of the platform and its operations are described by means of an example. A double evaluation of the extension is addressed, one qualitative and one quantitative. The results show that Excore reduces the number of interactions by 94.45% and the time to complete a task by 80.75%

Flow in multitasking : the effects of motivation, artifact, and task factors

textThe aims of this dissertation study are 1) to examine how the interplay of motivation, artifacts, and task interconnectedness affect users' flow experience, 2) to understand users' multitasking patterns by analyzing approaches and strategies in multitasking environments through a participatory design session, and 3) to come up with design insights and implications for desired multitasking environments based on findings from the quantitative and qualitative data analysis and synthesis. This dissertation employed the PAT (Person-Artifact-Task) model to examine factors that affect users' flow experience in computer-mediated multitasking environments. Particularly, this study focused on users' flow experience - sense of control, focused attention, curiosity, intrinsic interest and interactivity - in the context of multitasking. The dissertation begins with perspectives on human multitasking research from various disciplines. Emphasis is placed on how researchers have defined the term multitasking and the scope of previous multitasking research. In addition, this study provides definitions of the term task switching, which also has been used to describe human multitasking. The second section of this dissertation focuses on the literature, which characterizes factors and theoretical frameworks of human multitasking research. In this section, human multitasking factors were classified into internal and external factors to analyze factors from the micro to the macro perspective. More detailed definitions and comparisons are also addressed. To summarize and conclude the literature review, this study provides a synthesis framework of internal and external factors of human multitasking contexts. In section III, this dissertation introduces theoretical frameworks that include the constructs of the PAT (Person-Artifact-Task) model and flow model. The next three sections present the research design and two research methods - the experiment and participatory design. The results and discussion section includes the implications of interpreting people's flow experience with motivation, artifact (technology affordance type), and task interconnectedness through the PAT model. The study findings and implications should extend our understanding of multitasking behaviors and contexts and how the interplay of person, artifact, and task factors affects humans' flow experience. A concluding chapter explores future work and design implications on how researchers and designers can take contextual factors into consideration to identify the most effective multitasking in computer-mediated environments.Informatio

Timeout Reached, Session Ends?

Die Identifikation von Sessions zum Verständnis des Benutzerverhaltens ist ein Forschungsgebiet des Web Usage Mining. Definitionen und Konzepte werden seit über 20 Jahren diskutiert. Die Forschung zeigt, dass Session-Identifizierung kein willkürlicher Prozess sein sollte. Es gibt eine fragwürdige Tendenz zu vereinfachten mechanischen Sessions anstelle logischer Segmentierungen. Ziel der Dissertation ist es zu beweisen, wie unterschiedliche Session-Ansätze zu abweichenden Ergebnissen und Interpretationen führen. Die übergreifende Forschungsfrage lautet: Werden sich verschiedene Ansätze zur Session-Identifizierung auf Analyseergebnisse und Machine-Learning-Probleme auswirken? Ein methodischer Rahmen für die Durchführung, den Vergleich und die Evaluation von Sessions wird gegeben. Die Dissertation implementiert 135 Session-Ansätze in einem Jahr (2018) Daten einer deutschen Preisvergleichs-E-Commerce-Plattform. Die Umsetzung umfasst mechanische Konzepte, logische Konstrukte und die Kombination mehrerer Mechaniken. Es wird gezeigt, wie logische Sessions durch Embedding-Algorithmen aus Benutzersequenzen konstruiert werden: mit einem neuartigen Ansatz zur Identifizierung logischer Sessions, bei dem die thematische Nähe von Interaktionen anstelle von Suchanfragen allein verwendet wird. Alle Ansätze werden verglichen und quantitativ beschrieben sowie in drei Machine-Learning-Problemen (wie Recommendation) angewendet. Der Hauptbeitrag dieser Dissertation besteht darin, einen umfassenden Vergleich von Session-Identifikationsalgorithmen bereitzustellen. Die Arbeit bietet eine Methodik zum Implementieren, Analysieren und Evaluieren einer Auswahl von Mechaniken, die es ermöglichen, das Benutzerverhalten und die Auswirkungen von Session-Modellierung besser zu verstehen. Die Ergebnisse zeigen, dass unterschiedlich strukturierte Eingabedaten die Ergebnisse von Algorithmen oder Analysen drastisch verändern können.The identification of sessions as a means of understanding user behaviour is a common research area of web usage mining. Different definitions and concepts have been discussed for over 20 years: Research shows that session identification is not an arbitrary task. There is a tendency towards simplistic mechanical sessions instead of more complex logical segmentations, which is questionable. This dissertation aims to prove how the nature of differing session-identification approaches leads to diverging results and interpretations. The overarching research question asks: will different session-identification approaches impact analysis and machine learning tasks? A comprehensive methodological framework for implementing, comparing and evaluating sessions is given. The dissertation provides implementation guidelines for 135 session-identification approaches utilizing a complete year (2018) of traffic data from a German price-comparison e-commerce platform. The implementation includes mechanical concepts, logical constructs and the combination of multiple methods. It shows how logical sessions were constructed from user sequences by employing embedding algorithms on interaction logs; taking a novel approach to logical session identification by utilizing topical proximity of interactions instead of search queries alone. All approaches are compared and quantitatively described. The application in three machine-learning tasks (such as recommendation) is intended to show that using different sessions as input data has a marked impact on the outcome. The main contribution of this dissertation is to provide a comprehensive comparison of session-identification algorithms. The research provides a methodology to implement, analyse and compare a wide variety of mechanics, allowing to better understand user behaviour and the effects of session modelling. The main results show that differently structured input data may drastically change the results of algorithms or analysis

UNDERSTANDING, MODELING AND SUPPORTING CROSS-DEVICE WEB SEARCH

Recent studies have witnessed an increasing popularity of cross-device web search, in which users resume their previously-started search tasks from one device to later sessions on another. This novel search mode brings new user behaviors such as cross-device information transfer; however, they are rarely studied in recent research. Existing studies on this topic mainly focused on automatic cross-device search task extraction and/or task continuation prediction; whereas it lacks sufficient understanding of user behaviors and ways of supporting cross-device search tasks. Building an automated search support system requires proper models that can quantify user behaviors in the whole cross-device search process. This motivates me to focus on understanding, modeling and supporting cross-device search processes in this dissertation. To understand the cross-device search process, I examine the main cross-device search topics, the major triggers, the information transfer approaches, and users’ behavioral patterns within each device and across multiple devices. These are obtained through an on-line survey and a lab-controlled user study with fine-grained user behavior logs. Then, I work on two quantitative models to automatically capture users' behavioral patterns. Both models assume that user behaviors are driven by hidden factors, and the identified behavioral patterns are either the hidden factors or a reflection of hidden factors. Following prior studies, I consider two types of hidden factors --- search tactic (e.g., the tactic of information re-finding/finding would drive to click/skip previously-accessed documents) and user knowledge (e.g., knowing the knowledge within a document would drive users to skip the document). Finally, to create a real-world cross-device search support use case, I design two supporting functions: one to assist information re-finding and the other to support information finding. The effectiveness of different support functions are further examined through both off-line and on-line experiments. The dissertation has several contributions. First, this is the first comprehensive investigation of cross-device web search behaviors. Second, two novel computational models are proposed to automatically quantify cross-device search processes, which are rarely studied in existing researches. Third, I identify two important cross-device search support tasks and implement effective algorithms to support both of them, which can beneficial future studies for this topic

Beyond Searching: Understanding How People Use Search to Support Their Creative Endeavors

Creativity is an essential part of people's daily life and work across a range of everyday tasks. However, little prior work has explored how people use search engines and information resources as part of their creative processes, and how systems might better support users' information needs when working on tasks that involve creative endeavors. In this dissertation research, I sought to investigate the types of information seeking tools and strategies that people currently use in practice when they engage in projects that involve everyday creativity. The dissertation includes two parts. In the first part, an online survey with 175 participants was conducted to get a general understanding of how people use search engines and other existing information tools to support their everyday creativity tasks, the types of creative process stages that are involved in their tasks, and how they use different tools to support different creative stages. To get a deeper understanding of people's behaviors and their creative processes, in the second part, I conducted a two-week diary study to investigate users' in-situ search behaviors in their design-related projects from different perspectives (e.g., types of information sought in a project, intents to use the information found online, strategies of using different resources or tools in creative processes, and challenges encountered in creative processes). At the end of this dissertation, I discuss the implications of this research and provide recommendations for future research and the future design of search systems.Doctor of Philosoph

Inferring User Needs and Tasks from User Interactions

The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing users in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction. The major focus of this thesis is to work towards task based information retrieval systems - search systems which are adept at understanding, identifying and extracting tasks as well as supporting user’s complex search task missions. This thesis focuses on two major themes: (i) developing efficient algorithms for understanding and extracting search tasks from log user and (ii) leveraging the extracted task information to better serve the user via different applications. Based on log analysis on a tera-byte scale data from a real-world search engine, detailed analysis is provided on user interactions with search engines. On the task extraction side, two bayesian non-parametric methods are proposed to extract subtasks from a complex task and to recursively extract hierarchies of tasks and subtasks. A novel coupled matrix-tensor factorization model is proposed that represents user based on their topical interests and task behaviours. Beyond personalization, the thesis demonstrates that task information provides better context to learn from and proposes a novel neural task context embedding architecture to learn query representations. Finally, the thesis examines implicit signals of user interactions and considers the problem of predicting user’s satisfaction when engaged in complex search tasks. A unified multi-view deep sequential model is proposed to make query and task level satisfaction prediction

Designing Search User Interfaces for Visually Impaired Searchers: A User-centred Approach

PhDThe Web has been a blessing for visually impaired users as with the help of assistive technologies such as screen readers, they can access previously inaccessible information independently. However, for screen reader users, web-based information seeking can still be challenging as web pages are mainly designed for visual interaction. This affects visually impaired users’ perception of theWeb as an information space as well as their experience of search interfaces. The aim of this thesis is therefore to consider visually impaired users’ information seeking behaviour, abilities and interactions via screen readers in the design of a search interface to support complex information seeking. We first conduct a review of how visually impaired users navigate the Web using screen readers. We highlight the strategies employed, the challenges encountered and the solutions to enhance web navigation through screen readers. We then investigate the information seeking behaviour of visually impaired users on the Web through an observational study and we compare this behaviour to that of sighted users to examine the impact of screen reader interaction on the information seeking process. To engage visually impaired users in the design process, we propose and evaluate a novel participatory approach based on a narrative scenario and a dialogue-led interaction to verify user requirements and to brainstorm design ideas. The development of the search interface is informed by the requirements gathered from the observational study and is supported through the inclusion of visually impaired users in the design process. We implement and evaluate the proposed search interface with novel features to support visually impaired users for complex information seeking. This thesis shows that considerations for information seeking behaviour and users’ abilities and mode of interaction contribute significantly to the design of search user interfaces to ensure that interface components are accessible as well as usable

STOPPING AND RESUMING: HOW AND WHY DO PEOPLE SEARCH ACROSS SESSIONS FOR COMPLEX TASKS?

Cross-session searches (XSS) occur when people look for information online for multiple sessions to complete complex task goals over time. Previous studies explored aspects of XSS, including the reasons that lead to it, like the Multiple Information Seeking Episode (MISE) model, which highlights eight causes. However, less is known about how these reasons manifest in real-life XSS and their relationship with task characteristics. I conducted a diary study with 25 participants engaging in XSS for real-life tasks. Participants reported on at least three search sessions spanning at least two days, and 15 participants attended an interview after they completed the diary study. We used qualitative methods to explore motivations for expected XSS, goal complexity, session resuming and stopping reasons, types of found information, cognitive activities, and the non-search task activities that happened during the XSS process. Our results validated and refined the MISE session resuming and stopping reasons and distinguished subcategories and reasons unique to real-life XSS tasks. We discerned task-oriented and cognition-oriented motivations for XSS. We identified seven types of non-search task activities and three popular modes describing how people intertwine search and non-search activities during XSS. We assessed relationships among factors, including session goal complexity, information types, cognitive activities, session resuming, and stopping reasons using quantitative methods. Our results show significant associations between information types, cognitive activities, session goal complexity, and session resuming and stopping reasons. Furthermore, task stages significantly correlate with perceived overall task difficulty and the difficulty to find enough information. We also identified five XSS-specific challenges. Our results have implications for tailoring future search engines to customize search results according to session resuming reasons and designing tools to assist task management and preparation for session stops. Methodologically, our results have insights into designing tasks and subtasks and controlling the reasons that can lead to successive searches for tasks with varying complexity.Doctor of Philosoph