3,499 research outputs found

    Managing access to the internet in public libraries in the UK: the findings of the MAIPLE project

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    One of the key purposes of the public library is to provide access to information (UNESCO, 1994). In the UK, information is provided in printed formats and for the last decade via public access Internet workstations installed as part of the People’s Network initiative. Recent figures reveal that UK public libraries provide approximately 40,000 computer terminals offering users around 80,000 hours across more than 4,000 service points (CIPFA, 2012). In addition, increasing numbers of public libraries allow users to connect devices such as tablets or smart phones to the Internet via a wireless network access point (Wi-Fi). How do public library staff manage this? What about users viewing harmful or illegal content? And what are the implications for a profession committed to freedom of access to information and opposition to censorship? MAIPLE, a two-year project funded by the Arts and Humanities Research Council has been investigating this issue as little was known about how UK public libraries manage Internet content control including illegal material. MAIPLE has drawn on an extensive review of the literature, an online survey to which all UK public library services were invited to participate (39 per cent response rate) and case studies with five services (two in England, one in Scotland, one in Wales and one in Northern Ireland) to examine the ways these issues are managed and their implications for staff. This presentation will explore the prevalence of tools such as filtering software, Acceptable Use Policies, user authentication, booking software and visual monitoring by staff and consider their efficacy and desirability in the provision of public Internet access. It will consider the professional dilemmas inherent within managing content and access. Finally, it will highlight some of the more important themes emerging from the findings and their implications for practitioners and policy makers

    Recommender Systems

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    The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

    The interaction between humans and knowledge management systems : rethinking the future

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    In this workshop position paper, we propose a study to understand the importance of knowledge management systems among academics in Saudi higher education institutions, admitting that knowledge workers and Knowledge Management Systems are valuable organizational assets whose interaction should be improved. We intend to understand Saudi academics’ perception toward using the knowledge management system to share their teaching experiences. Based on the findings, we investigate the major research trends in knowledge management systems and give some recommendations for future research

    What’s going on in my city? Recommender systems and electronic participatory budgeting

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    In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

    A Service-based Model for Customer Intelligence in the Age of Big Data

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    The dominance of the service sector in today’s economy gives prominence to customer intelligence as a means for enterprises to provide optimal service. In fact, the revolution of big data has generated a vast amount of customer data and reshaped the dimensions of science, management, and engineering within enterprises. The big data era also acknowledges the role of customers as value co-creators. Therefore, the objective of this paper is to propose a service-based customer intelligence model, hereafter called SBCI (Service-based Customer Intelligence) model, to guide the development and application of customer intelligence. Laid the groundwork upon the service science, the model is proposed with three levels: i) the network of service systems level for customer value co-creation, ii) the service system level for the science, management, and engineering dimensions, and iii) the service level for customer intelligence services

    Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

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    This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

    Personalization in cultural heritage: the road travelled and the one ahead

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    Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

    A Cluster-based Recommender System

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    Introduction: E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. Objectives: The aim of this project is to provide a cluster-based recommender system which cluster users based on their history (previous interactions with the system) to increase the accuracy of recommendations. Method: The proposed approach consists of two phases: offline and online. In the offline phase, users are clustered using genetic algorithm. In the online phase, the appropriate cluster or clusters and neighborhood are selected for the target user. Then, his/her interesting items (not chosen yet) are determined using interesting items of his/her neighbors. Results: After implementing the proposed approach for the recommender system, it was evaluated in terms of accuracy (the portion of recommended items which have been interesting for the users) and compared it with several existing recommender systems. The results show that our approach outperforms other approaches. Conclusions: Having a good recommender system encourages users to buy new products, find new friends, or watch new videos. On the contrary, an inaccurate recommender system may discourage the users and motivates them to sign out of the system or ignore all recommendations. The approach we proposed for recommendation achieved promising results. We hope by completing the project we can use this approach in developing commercial recommender systems
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