Distributed enterprise search using software agents

In this paper we introduce a distributed information retrieval system using agent-based technology. In this multiagent system, each agent has its own specific task and can be used to handle a specific document repository. The system is designed to automatically comply with access restriction rules that are normally enforced in companies. It is used in the administration offices of the German capital city Berlin where it serves as a testbed for further research on aggregated search in an enterprise environment with roughly 50,000 employees

Benchmarking News Recommendations in a Living Lab

Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems

New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data

In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future

Providing Multilingual Access to Health-Oriented Content

Finding health-related content is not an easy task. People have to know what to search for, which medical terms to use, and where to find accurate information. This task becomes even harder when people such as immigrants wish to find information in their country of residence and do not speak the national language very well. In this paper, we present a new health information system that allows users to search for health information using natural language queries composed of multiple languages. We present the technical details of the system and outline the results of a preliminary user study to demonstrate the usability of the system

New Frontiers of Quantified Self 3: Exploring Understudied Categories of Users

Quantified Self (QS) field needs to start thinking of how situated needs may affect the use of self-tracking technologies. In this workshop we will focus on the idiosyncrasies of specific categories of users

Users and noise: The magic barrier of recommender systems

Abstract. Recommender systems are crucial components of most commercial web sites to keep users satisfied and to increase revenue. Thus, a lot of effort is made to improve recommendation accuracy. But when is the best possible performance of the recommender reached? The magic barrier, refers to some unknown level of prediction accuracy a recommender system can attain. The magic barrier reveals whether there is still room for improving prediction accuracy, or indicates that any further improvement is meaningless. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies -noise. In a case study with a commercial movie recommender, we investigate the inconsistencies of the user ratings and estimate the magic barrier in order to assess the actual quality of the recommender system

Providing multilingual access to health-related content

Finding health-related content is not an easy task. People have to know what to search for, which medical terms to use, and where to find accurate information. This task becomes even harder when people such as immigrants wish to find information in their country of residence and do not speak the national language very well. In this paper, we present a new health information system that allows users to search for health information using natural language queries composed of multiple languages. We present the technical details of the system and outline the results of a preliminary user study to demonstrate the usability of the system

User Modeling im semantischen, sozialen Web

With the rise of the Web in the 1990’s, people got access to a yet unknown amount of information, finding themselves in the role of consumers of information. Since then, information on the Web has grown exponentially. All kinds of information - good, bad, incorrect, outdated or spam - can be found on the Web. With the availability of web-enabled mobile phones, people got ubiquitous access to this information wealth. This ubiquity of information access in our everyday life offers not only new opportunities but comes with more and more challenges to deal with. Finding relevant information becomes more and more difficult. This effect is known as the Information Overload problem. The problem describes the fact that humans have cognitive limits to process information. To much information makes it hard to understand a topic and to make decisions. While there are tools to support users in finding information, e.g., search engines, filtering for the relevant information is still a task for every user individually. Today, new technologies and approaches are needed to overcome the Information Overload problem and to support users in finding the way through all available information and deliver only the information needed. A promising approach is the application of adaptive systems. Adaptive systems, in a broader scope, are systems that help users to satisfy their information need by adapting the system and/or the displayed information to specific user requirements and therefore reducing the Information Overload problem. An adaptive system can be divided into three main layers: The data acquisition layer: In the data acquisition layer, all available information about a user is collected. The representation and data mining layer: The data collected in the previous layer is processed and used to build a user profile. The adaptation layer: The adaptation layer applies the user profile to adapt the application to the user needs. In this thesis, we examine how the ”SemanticWeb” and the ”SocialWeb” can enhance adaptive systems with the goal to solve the Information Overload problem. The Social Web, represented by applications such as Facebook or Twitter, enables users to express their needs and preferences. The Semantic Web provides techniques allowing to manage data in machine readable form. In this work, we develop methods and models to collect and process data from the Social Web to enhance personalization. Semantic Web technologies are used to manage the data and allow us to use it application independent. Thereby, data can be used across different applications and thus more knowledge about the user is available. The developed methods and models are applied and demonstrated in three online systems, which were developed throughout this thesis.Mit dem Beginn des Internetzeitalters, Anfang der 90er Jahre, stieg die Menge an verfügbaren Informationen sprunghaft an, und steigt seitdem exponentiell weiter. Dabei sind alle Arten von Informationen vorhanden - wichtige, unwichtige, richtige, falsche oder auch veraltete. Mit der Verfügbarkeit von internetfähigen Mobiltelefonen sind diese Informationen nun auch rund um die Uhr und überall verfügbar. Diese allgegenwärtige Verfügbarkeit hat allerdings nicht nur Vorteile. Das Finden von relevanten Informationen wird immer schwieriger. Man spricht dabei auch vom Information Overload Problem. Das Information Overload Problem beschreibt die Problematik, das Menschen nur begrenzte kognitive Fähigkeiten haben, um Informationen zu verarbeiten. Bei zu vielen Informationen kann dann der Mensch diese nicht mehr verstehen und Entscheidungen treffen. Es gibt zwar Anwendungen, die das Finden von Informationen unterstützen, z.B. Suchmaschinen, aber das Filtern nach relevanten Informationen obliegt dabei immer noch den Nutzern. Um das Problem des Information Overloads zu lösen, unterstützen Anwendungen den Nutzer mit Personalisierungsmechanismen. Systeme, die sich an die Präferenzen des Nutzers anpassen, um dessen Informationsbedürfnis zu befriedigen, nennt man Adaptive Systeme. Ein adaptives System wird im Allgemeinen in drei Schichten unterteilt: Die Daten-Aggregationsschicht: In dieser Schicht werden Daten über den Nutzer gesammelt, für den eine Anwendung personalisiert werden soll. Die Repräsentations- und Analyseschicht: In dieser Ebene werden die gesammelten Daten des Nutzers verwaltet und aufbereitet. Es wird aus den gesammelten Daten ein Benutzerprofile und Abneigungen des Nutzers erstellt. Die Adaptionsschicht: Diese Schicht repräsentiert die eigentliche Personalisierung einer Anwendung. Basierend auf dem erstellten Benutzerprofile wird eine Anwendung, inhaltlich oder in der Visualisierung, an die Präferenzen des Nutzers angepasst. Im Rahmen dieser Arbeit wird untersucht, wie adaptive System durch das Social Web und das Semantic Web verbessert werden können, um das Problem des Information Overloads zu lösen. Das Social Web, repräsentiert durch Anwendungen wie Facebook oder Twitter, erlaubt es Nutzern, eigene Interessen und Präferenzen auszudrücken. Das Semantic Web bietet Technologien, die es erlauben, Daten maschinenlesbar zu verwalten. In dieser Arbeit werden Modelle und Methoden eingeführt, die Daten aus dem Social Web verarbeiten, um eine verbesserte Personalisierung zu ermöglichen. Dabei werden die Daten aus dem Social Web mittels Technologien des Semantic Web verwaltet und sind applikationsübergreifend verwendbar. Dadurch stehen mehr Daten über den Nutzer zur Verfügung, was eine bessere Personalisierung erlaubt. Diese Modelle und Methoden werden in drei Onlinesystemen demonstriert und evaluiert, die im Rahmen dieser Arbeit entwickelt wurden

Perceived and actual role of gamification principles

Although gamification has successfully been applied in office scenarios, it remains unclear how employees really feel about the introduction of a gamified system at their workplace. In this paper, we address this issue from two directions. First, we present the outcome of an online survey where we analyze users' opinion about gamification in a workplace environment. Then, we analyze the interaction logs of a re-designed gamified enterprise book marking system to compare the employees' subjective perception of gamification with their actual behavior when using a gamified system. Results indicate that there is a strong relationship between employees' perception of gamification and their actual interaction with such system