A Reference Model For Mobile Product Information Systems

This paper analyses the state of the art in research and practice on mobile product information systems. Based on literature review and multiple case study research, we design a reference model that is suitable for researchers and practitioners as a first reference point and recommendation for the construction and analysis of mobile product information systems

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

Tests comparatifs d'utilisabilité des plateformes Swissbib.ch et Data.bnf.fr

L’apparition des plateformes en Linked Open Data, permet d’entrevoir de nouvelles possibilités d’accès à l’information et offre de nouvelles opportunités en termes d’utilisabilité. Ainsi, ce travail est réalisé dans le cadre du projet linked.swissbib.ch qui vise à enrichir le catalogue swissbib.ch grâce au web sémantique. Le but de ce travail de bachelor est par le biais de tests comparatifs d’évaluer la satisfaction des utilisateurs vis-à-vis de ce nouveau type d’outil de recherche et la manière dont ils s’approprient ce dernier, face à l’utilisation d’un catalogue « standard » de recherche. Ainsi, ces tests se focalisent sur la plateforme data.bnf.fr développée en Linked Open Data et le méta catalogue swissbib.ch. L’objectif de cette méthode est de recueillir des données permettant la formulation de recommandations pour le développement de fonctionnalités en Linked Open Data pour la plateforme swissbib.ch. A travers ce mémoire, le processus qui a permis d’élaborer ce test dans l’objectif de répondre aux besoins du mandat est présenté de même que les résultats obtenus. Ces derniers mettent en avant le potentiel d’une plateforme en Linked Open Data face à l’utilisation d’un catalogue standard de recherche. En effet, il apparaît que la plateforme data.bnf.fr de par sa structure et ses fonctionnalités semble faciliter le processus de prise en main de l’outil et a un impact positif sur la satisfaction des utilisateurs par rapport à l’utilisation du méta catalogue swissbib.ch. Par conséquent, la plateforme data.bnf.fr semble être un modèle intéressant à suivre dans le cadre du développement de fonctionnalités en web sémantique pour swissbib.ch. Toutefois, les tests ont révélé également des éléments auxquels il faut être attentif dans une logique de structure telle qu’on peut la trouver sur data.bnf.fr. Enfin, ce travail a esquissé le potentiel de prise en main d’une plateforme LOD, notamment pour les participants n’étant pas rodés à l’utilisation des catalogues de bibliothèques. Cet aspect met en avant le potentiel du Linked Open Data afin de rendre les catalogues de bibliothèques davantage « user friendly »

Information journeys in digital archives

Archival collections have particular properties that make physical and intellectual access difficult for researchers. This generates feelings of uncertainty in the researchers leading to a large burden of enquiries to the archive, many routine. In this thesis I investigate the information seeking behaviours of archival researchers and the distinct properties of the archive first through the respective literatures and then through a series of five studies. Using systems, data and researchers from the National Archives, these studies examine the nature of the enquiries archives receive across many channels, the in-person interactions between archivists and researchers in the reading rooms and the unmediated search behaviours of archival researchers. I proceed to outline the barriers inhibiting research progress and the techniques or 'regulators' used by researchers to surmount or mitigate these barriers. In the final two studies I develop and attempt to validate an instrument for measuring uncertainty in information seeking in large digital collections. This three factor (disorientation, prospect and preparedness) scale of archival uncertainty allows improvements to online archival systems to be effectively tested before implementation. I also propose system properties which seem likely to assist researchers to make progress given these factors and which could be tested using this instrument

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