A laboratory-based method for the evaluation of personalised search

Comparative evaluation of Information Retrieval Systems (IRSs) using publically available test collections has become an established practice in Information Retrieval (IR). By means of the popular Cranfield evaluation paradigm IR test collections enable researchers to compare new methods to existing approaches. An important area of IR research where this strategy has not been applied to date is Personalised Information Retrieval (PIR), which has generally relied on user-based evaluations. This paper describes a method that enables the creation of publically available extended test collections to allow repeatable laboratory-based evaluation of personalised search

A proposal for the evaluation of adaptive information retrieval systems using simulated interaction

The Centre for Next Generation Localisation (CNGL) is involved in building interactive adaptive systems which combine Information Retrieval (IR), Adaptive Hypermedia (AH) and adaptive web techniques and technologies. The complex functionality of these systems coupled with the variety of potential users means that the experiments necessary to evaluate such systems are difficult to plan, implement and execute. This evaluation requires both component-level scientific evaluation and user-based evaluation. Automated replication of experiments and simulation of user interaction would be hugely beneficial in the evaluation of adaptive information retrieval systems (AIRS). This paper proposes a methodology for the evaluation of AIRS which leverages simulated interaction. The hybrid approach detailed combines: (i) user-centred methods for simulating interaction and personalisation; (ii) evaluation metrics that combine Human Computer Interaction (HCI), AH and IR techniques; and (iii) the use of qualitative and quantitative evaluations. The benefits and limitations of evaluations based on user simulations are also discussed

On User Modelling for Personalised News Video Recommendation

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

Ensuring sample quality for biomarker discovery studies - Use of ict tools to trace biosample life-cycle

The growing demand of personalized medicine marked the transition from an empirical medicine to a molecular one, aimed at predicting safer and more effective medical treatment for every patient, while minimizing adverse effects. This passage has emphasized the importance of biomarker discovery studies, and has led sample availability to assume a crucial role in biomedical research. Accordingly, a great interest in Biological Bank science has grown concomitantly. In biobanks, biological material and its accompanying data are collected, handled and stored in accordance with standard operating procedures (SOPs) and existing legislation. Sample quality is ensured by adherence to SOPs and sample whole life-cycle can be recorded by innovative tracking systems employing information technology (IT) tools for monitoring storage conditions and characterization of vast amount of data. All the above will ensure proper sample exchangeability among research facilities and will represent the starting point of all future personalized medicine-based clinical trials

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

Personalised service? Changing the role of the government librarian

Investigates the feasibility of personalised information service in a government department. A qualitative methodology explored stakeholder opinions on the remit, marketing, resourcing and measurement of the service. A questionnaire and interviews gathered experiences of personalised provision across the government sector. Potential users were similarly surveyed to discuss how the service could meet their needs. Data were analysed using coding techniques to identify emerging theory. Lessons learned from government librarians centred on clarifying requirements, balancing workloads and selective marketing. The user survey showed low usage and awareness of existing specialist services, but high levels of need and interest in services repackaged as a tailored offering. Fieldwork confirmed findings from the literature on the scope for adding value through information management advice, information skills training and substantive research assistance and the need to understand business processes and develop effective partnerships. Concluding recommendations focus on service definition, strategic marketing, resource utilisation and performance measurement

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts