David De Roure

Ontological user profiling in recommender systems

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

The design and realisation of the Experimentmy Virtual Research Environment for social sharing of workflows

In this paper we suggest that the full scientific potential of workflows will be achieved through mechanisms for sharing and collaboration, empowering scientists to spread their experimental protocols and to benefit from those of others. To facilitate this process we have designed and built the Experimentmy Virtual Research Environment for collaboration and sharing of workflows and experiments. In contrast to systems which simply make workflows available, Experimentmy provides mechanisms to support the sharing of workflows within and across multiple communities. It achieves this by adopting a social web approach which is tailored to the particular needs of the scientist. We present the motivation, design and realisation of Experimentmy.

myExperiment: a repository and social network for the sharing of bioinformatics workflows

myExperiment (http://www.myexperiment.org) is an online research environment that supports the social sharing of bioinformatics workflows. These workflows are procedures consisting of a series of computational tasks using web services, which may be performed on data from its retrieval, integration and analysis, to the visualization of the results. As a public repository of workflows, myExperiment allows anybody to discover those that are relevant to their research, which can then be reused and repurposed to their specific requirements. Conversely, developers can submit their workflows to myExperiment and enable them to be shared in a secure manner. Since its release in 2007, myExperiment currently has over 3500 registered users and contains more than 1000 workflows. The social aspect to the sharing of these workflows is facilitated by registered users forming virtual communities bound together by a common interest or research project. Contributors of workflows can build their reputation within these communities by receiving feedback and credit from individuals who reuse their work. Further documentation about myExperiment including its REST web service is available from http://wiki.myexperiment.org. Feedback and requests for support can be sent to bugs@myexperiment.org.

Capturing knowledge of user preferences: ontologies in recommender systems

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.

myExperiment: social networking for workflow-using e-scientists

We present the Taverna workflow workbench and argue that scientific workflow environments need a rich ecosystem of tools that support the scientists. experimental lifecycle. Workflows are scientific objects in their own right, to be exchanged and reused. myExperiment is a new initiative to create a social networking environment for workflow workers. We present the motivation for myExperiment and sketch the proposed capabilities and challenges. We argue that actively engaging with a scientists needs, fears and reward incentives is crucial for success.

Ontology-based Recommender Systems

We present an overview of the latest approaches to using ontologies in recommender systems and our work on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy.

Transparent Fault Tolerance for Web Services Based Architectures

Service-based architectures enable the development of new classes of Grid and distributed applications. One of the main capabilities provided by such systems is the dynamic and flexible integration of services, according to which services are allowed to be a part of more than one distributed system and simultaneously serve different applications. This increased flexibility in system composition makes it difficult to address classical distributed system issues such as fault-tolerance. While it is relatively easy to make an individual service fault-tolerant, improving fault-tolerance of services collaborating in multiple application scenarios is a challenging task. In this paper, we look at the issue of developing fault-tolerant service-based distributed systems, and propose an infrastructure to implement fault tolerance capabilities transparent to services.

Experiences with GRIA — Industrial Applications on a Web Services Grid

The GRIA project set out to make the grid usable by industry. The GRIA middleware is based on Web services, and designed to meet the needs of industry for security and business-to-business (B2B) service procurement and operation. It provides well-defined B2B models for accounting and QoS agreement, and proxy-free delegation to support account management and service federation. The GRIA v3 software is now being used by industry. By taking a business-oriented approach independent of the evolving Open Grid Services Architecture proposals from the Global Grid Forum, GRIA has demonstrated the need for a wider understanding of virtual organizations (VOs). Traditional academic VOs are persistent, resourceful and have logically centralized, membership-oriented management structures. In contrast, the GRIA experience has been that business VOs are likely to be project-focused and have distributed process-oriented management structures

Taverna Workflows: Syntax and Semantics

This paper presents the formal syntax and the operational semantics of Taverna, a workflow management system with a large user base among the e-Science community. Such formal foundation, which has so far been lacking, opens the way to the translation between Taverna workflows and other process models. In particular, the ability to automatically compile a simple domain-specific process description into Taverna facilitates its adoption by e-scientists who are not expert workflow developers. We demonstrate this potential through a practical use case.

Capturing interest through inference and visualization: ontological user profiling in recommender systems

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems help where explicit search queries are not available or are difficult to formulate, learning the type of thing users like over a period of time.We explore an ontological approach to user profiling in the context of a recommender system. Building on previous work involving ontological profile inference and the use of external ontologies to overcome the cold-start problem, we explore the idea of profile visualization to capture further knowledge about user interests. Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers. Both our ontological approach to user profiling and our visualization of user profiles are novel ideas to recommender systems. A year long experiment is conducted with over 200 staff and students at the University of Southampton. The effectiveness of visualizing profiles and eliciting profile feedback is measured, as is the overall effectiveness of the recommender system.