Mutual information based clustering of market basket data for profiling users

Attraction and commercial success of web sites depend heavily on the additional values visitors may find. Here, individual, automatically obtained and maintained user profiles are the key for user satisfaction. This contribution shows for the example of a cooking information site how user profiles might be obtained using category information provided by cooking recipes. It is shown that metrical distance functions and standard clustering procedures lead to erroneous results. Instead, we propose a new mutual information based clustering approach and outline its implications for the example of user profiling

The application development process: What role does it play in the success of an application for the user developer?

End user development of applications forms a significant part of organisational systems development. This study investigates the role that developing an application plays in the eventual success of the application for the user developer. The results of this study suggest that the process of developing an application not only predisposes an end user developer to be more satisfied with the application than they would be if it were developed by another end user, but also leads them to perform better with it. Thus the results of the study highlight the contribution of the process of application development to application success

Sticks, balls or a ribbon? Results of a formative user study with bioinformaticians

User interfaces in modern bioinformatics tools are designed for experts. They are too complicated for\ud novice users such as bench biologists. This report presents the full results of a formative user study as part of a\ud domain and requirements analysis to enhance user interfaces and collaborative environments for\ud multidisciplinary teamwork. Contextual field observations, questionnaires and interviews with bioinformatics\ud researchers of different levels of expertise and various backgrounds were performed in order to gain insight into\ud their needs and working practices. The analysed results are presented as a user profile description and user\ud requirements for designing user interfaces that support the collaboration of multidisciplinary research teams in\ud scientific collaborative environments. Although the number of participants limits the generalisability of the\ud findings, the combination of recurrent observations with other user analysis techniques in real-life settings\ud makes the contribution of this user study novel

Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built

Using mobile RE tools to give end-users their own voice

Researchers highlight end-user involvement in system design as an important concept for developing useful and usable solutions. However, end-user involvement in software engineering is still an open-ended topic. Novel paradigms such as service-oriented computing strengthen the need for more active end-user involvement in order to provide systems that are tailored to individual end-user needs. Our work is based on the fact that the majority of end-users are familiar with mobile devices and use an increasing number of mobile applications. A mobile tool enabling end-user led requirements elicitation could be just one of many applications installed on end-users' mobile devices. In this paper, we present a framework of end-user involvement in requirements elicitation which motivates our research. The main contribution of our research is a tool-supported requirements elicitation approach allowing end-users to document needs in situ. Furthermore, we present first evaluation results to highlight the feasibility of on-site end-user led requirements elicitation

Journal Staff

In this contribution we describe some of the basic new features of MathWork's System Identification toolbox, version 4.0, which was released in May 1995. The main addition is a graphical user interface (GUI), which allows the user to perform identification, data and model analysis, as well as model validation by less click and mouseless operations. The ideas behind the GUI are explained and its relative merits compared to command driven operations are discussed

DeepCity: A Feature Learning Framework for Mining Location Check-ins

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly

User Applications Driven by the Community Contribution Framework MPContribs in the Materials Project

This work discusses how the MPContribs framework in the Materials Project (MP) allows user-contributed data to be shown and analyzed alongside the core MP database. The Materials Project is a searchable database of electronic structure properties of over 65,000 bulk solid materials that is accessible through a web-based science-gateway. We describe the motivation for enabling user contributions to the materials data and present the framework's features and challenges in the context of two real applications. These use-cases illustrate how scientific collaborations can build applications with their own "user-contributed" data using MPContribs. The Nanoporous Materials Explorer application provides a unique search interface to a novel dataset of hundreds of thousands of materials, each with tables of user-contributed values related to material adsorption and density at varying temperature and pressure. The Unified Theoretical and Experimental x-ray Spectroscopy application discusses a full workflow for the association, dissemination and combined analyses of experimental data from the Advanced Light Source with MP's theoretical core data, using MPContribs tools for data formatting, management and exploration. The capabilities being developed for these collaborations are serving as the model for how new materials data can be incorporated into the Materials Project website with minimal staff overhead while giving powerful tools for data search and display to the user community.Comment: 12 pages, 5 figures, Proceedings of 10th Gateway Computing Environments Workshop (2015), to be published in "Concurrency in Computation: Practice and Experience

Firms' contribution to open source software and the dominant skilled user

: Free/libre or open-source software (FLOSS) is nowadays produced not only by individual benevolent developers but, in a growing proportion, by firms that hire programmers for their own objectives of development in open source or for contributing to open-source projects in the context of dedicated communities. A recent literature has focused on the question of the business models explaining how and why firms may draw benefits from such involvement and their connected activities. They can be considered as the building blocks of a new modus operandi of an industry, built on an alternative approach to intellectual property management. Its prospects will depend on both the firms' willingness to rally and its ability to compete with the traditional “proprietary” approach. As a matter of fact, firms' involvement in FLOSS, while growing, remains very contrasting, depending on the nature of the products and the characteristics of the markets. The aim of this paper is to emphasize that, beside factors like the importance of software as a core competence of the firm, the role of users on the related markets - and more precisely their level of skills - may provide a major explanation of such diversity. We introduce the concept of the dominant skilled user and we set up a theoretical model to better understand how it may condition the nature and outcome of the competition between a FLOSS firm and a proprietary firm. We discuss these results in the light of empirical stylized facts drawn from the recent trends in the software industrySoftware ; Open Source ; Intellectual Property ; Competition ; Users