3,380 research outputs found

    The Ubiquitous Interactor - Device Independent Access to Mobile Services

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    The Ubiquitous Interactor (UBI) addresses the problems of design and development that arise around services that need to be accessed from many different devices. In UBI, the same service can present itself with different user interfaces on different devices. This is done by separating interaction between users and services from presentation. The interaction is kept the same for all devices, and different presentation information is provided for different devices. This way, tailored user interfaces for many different devices can be created without multiplying development and maintenance work. In this paper we describe the system design of UBI, the system implementation, and two services implemented for the system: a calendar service and a stockbroker service

    Engaging end-user driven recommender systems: personalization through web augmentation

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    In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.Fil: Wischenbart, Martin. Johannes Kepler University Linz; AustriaFil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austri

    Client Side Privacy Protection Using Personalized Web Search

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    AbstractWe are providing a Client-side privacy protection for personalized web search.. Any PWS captures user profiles in a hierarchical taxonomy. The system is performing online generalization on user profiles to protect the personal privacy without compromising the search quality and attempt to improve the search quality with the personalization utility of the user profile. On other side they need to hide the privacy contents existing in the user profile to place the privacy risk under control. User privacy can be provided in form of protection like without compromising the personalized search quality. In general we are working for a trade off between the search quality and the level of privacy protection achieved from generalization

    Mapping web personal learning environments

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    A recent trend in web development is to build platforms which are carefully designed to host a plurality of software components (sometimes called widgets or plugins) which can be organized or combined (mashed-up) at user's convenience to create personalized environments. The same holds true for the web development of educational applications. The degree of personalization can depend on the role of users such as in traditional virtual learning environment, where the components are chosen by a teacher in the context of a course. Or, it can be more opened as in a so-called personalized learning environment (PLE). It now exists a wide array of available web platforms exhibiting different functionalities but all built on the same concept of aggregating components together to support different tasks and scenarios. There is now an overlap between the development of PLE and the more generic developments in web 2.0 applications such as social network sites. This article shows that 6 more or less independent dimensions allow to map the functionalities of these platforms: the screen dimensionmaps the visual integration, the data dimension maps the portability of data, the temporal dimension maps the coupling between participants, the social dimension maps the grouping of users, the activity dimension maps the structuring of end users–interactions with the environment, and the runtime dimensionmaps the flexibility in accessing the system from different end points. Finally these dimensions are used to compare 6 familiar Web platforms which could potentially be used in the construction of a PLE

    Web Mining for Web Personalization

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    Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

    Resist Adversary in Modified Net Explore

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    In this paper, user profiles, portrayals of user supplies, can be absorbed via search engine for to give customized look for results. Rich techniques capture user for building user information through proxies web servers (to catch scanning histories).These jointly need servicing of the user to provide the proxies server. In this reading, we examine the consumption of a less-invasive means modifying to unclear concerns has extended been an important aspect in the analysis of Data Recovery. Personalized look for has as of late got amazing regard for location this analyze in the web search set, in light of the begin that a user’s general sensation might help the search engine for disambiguate the legitimate plan of an query. The customized look for has been suggested for some a long time and many customization methods have been researched, it is still unclear whether customization is effectively practical on different questions for unique users, and under unique search configurations. In this paper, we focus on how to infer a user’s attention from the user’s search connection and usage the deduced certain user design for customized search. We analyzed defense insurance in PWS applications that design user tendency as modern user information. This system suggested a PWS framework called UPS that can adaptively sum up information by reviews although regarding user mentioned protection requirements. We confirmed two greedy computations, in certain GreedyDP what’s more GreedyIL, for runtime rumors. We will avoid opponents with wider history knowledge, such as richer connection among subjects or capability to catch a series of queries from the victim. We will also search for more innovative technique to build the user information, and better analytics to estimate the efficiency of UPS. DOI: 10.17762/ijritcc2321-8169.15071

    Where are your Manners? Sharing Best Community Practices in the Web 2.0

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    The Web 2.0 fosters the creation of communities by offering users a wide array of social software tools. While the success of these tools is based on their ability to support different interaction patterns among users by imposing as few limitations as possible, the communities they support are not free of rules (just think about the posting rules in a community forum or the editing rules in a thematic wiki). In this paper we propose a framework for the sharing of best community practices in the form of a (potentially rule-based) annotation layer that can be integrated with existing Web 2.0 community tools (with specific focus on wikis). This solution is characterized by minimal intrusiveness and plays nicely within the open spirit of the Web 2.0 by providing users with behavioral hints rather than by enforcing the strict adherence to a set of rules.Comment: ACM symposium on Applied Computing, Honolulu : \'Etats-Unis d'Am\'erique (2009
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