Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Analytics of human presence and movement behaviour within specific environments

The vast amounts of detailed information, generated by Wi-Fi and other mobile communication technologies, provide an invaluable opportunity to study different aspects of presence and movement behaviours of people within a given environment; for example, a university campus, an organisation office complex, or a city centre. Utilising such data, this thesis studies three main aspects of the human presence and movement behaviours: spatio-temporal movement (where and when do people move), user identification (how to uniquely identify people from their presence and movement historical records), and social grouping (how do people interact). Previous research works have predominantly studied two out of these three aspects, at most. Conversely, we investigate all three aspects in order to develop a coherent view of the human presence and movement behaviour within selected environments. More specifically, we create stochastic models for movement prediction and user identification. We also devise a set of clustering models for the detection of the social groups within a given environment. The thesis makes the following contributions: 1. Proposes a family of predictive models that allows for inference of locations though a collaborative mechanism which does not require the profiling of individual users. These prediction models utilise suffix trees as their core underlying data structure, where predictions about a specific individual are computed over an aggregate model incorporating the collective record of observed behaviours of multiple users. 2. Defines a mobility fingerprint as a profile constructed from the users historical mobility traces. The proposed method for constructing such a profile is a principled and scalable implementation of a variable length Markov model based on n-grams. 3. Proposes density-based clustering methods that discover social groups by analysing activity traces of mobile users as they move around, from one location to another, within an observed environment. We utilise two large collections of mobility traces: a GPS data set from Nokia and an Eduroam network log from Birkbeck, University of London, for the evaluation of the proposed models reported herein

Analytics of human presence and movement behaviour within specific environments

The vast amounts of detailed information, generated by Wi-Fi and other mobile communication technologies, provide an invaluable opportunity to study different aspects of presence and movement behaviours of people within a given environment; for example, a university campus, an organisation office complex, or a city centre. Utilising such data, this thesis studies three main aspects of the human presence and movement behaviours: spatio-temporal movement (where and when do people move), user identification (how to uniquely identify people from their presence and movement historical records), and social grouping (how do people interact). Previous research works have predominantly studied two out of these three aspects, at most. Conversely, we investigate all three aspects in order to develop a coherent view of the human presence and movement behaviour within selected environments. More specifically, we create stochastic models for movement prediction and user identification. We also devise a set of clustering models for the detection of the social groups within a given environment. The thesis makes the following contributions: 1. Proposes a family of predictive models that allows for inference of locations though a collaborative mechanism which does not require the profiling of individual users. These prediction models utilise suffix trees as their core underlying data structure, where predictions about a specific individual are computed over an aggregate model incorporating the collective record of observed behaviours of multiple users. 2. Defines a mobility fingerprint as a profile constructed from the users historical mobility traces. The proposed method for constructing such a profile is a principled and scalable implementation of a variable length Markov model based on n-grams. 3. Proposes density-based clustering methods that discover social groups by analysing activity traces of mobile users as they move around, from one location to another, within an observed environment. We utilise two large collections of mobility traces: a GPS data set from Nokia and an Eduroam network log from Birkbeck, University of London, for the evaluation of the proposed models reported herein

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

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

A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams

Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag