Personalization for unobtrusive service interaction

Gil, Míriam; Giner, Pau; Pelechano, Vicente

doi:10.1007/s00779-011-0414-0

Cited by 21 publications

(20 citation statements)

References 38 publications

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“…In order to address this issue, we represent the adaptation space in terms of human limitations. Specifically, we use the conceptual framework for designing unobtrusive interactions to define our unobtrusive adaptation space [7].…”

Section: Healthcare Servicementioning

confidence: 99%

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Self-adaptive unobtrusive interactions of mobile computing systems

Gil

Pelechano

2017

AIS

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In Pervasive Computing environments, people are surrounded by a lot of embedded services. Since pervasive devices, such as mobile devices, have become a key part of our everyday life, they enable users to always be connected to the environment, making demands on one of the most valuable resources of users: human attention. A challenge of the mobile computing systems is regulating the request for users' attention. In other words, service interactions should behave in a considerate manner by taking into account the degree to which each service intrudes on the user's mind (i.e., the degree of obtrusiveness). The main goal of this paper is to introduce self-adaptive capabilities in mobile computing systems in order to provide non-disturbing interactions. We achieve this by means of an software infrastructure that automatically adapts the service interaction obtrusiveness according to the user's context. This infrastructure works from a set of high-level models that define the unobtrusive adaptation behavior and its implication with the interaction resources in a technology-independent way. Our infrastructure has been validated through several experiments to assess its correctness, performance, and the achieved user experience through a user study.

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Section: Healthcare Servicementioning

confidence: 99%

“…In earlier work, Gil et al [7] presented a design method to capture the degree of obtrusiveness required to interact with a service based on the user's needs. By considering the obtrusiveness aspects in a static manner, personalized interactions of services can be provided, but always with the same degree of obtrusiveness.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive unobtrusive interactions of mobile computing systems

Gil

Pelechano

2017

AIS

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“…Data collection from mobile devices -and where the collection does not require an explicit consent from the carrier/individual -is often named "unobtrusive" [13]. Typical applications may include visitor flow analysis in shopping malls [14], [15], [16] or location services to help a visitor find places [17], [18].…”

Section: Collecting Datamentioning

confidence: 99%

Reality mining in the classroom

Svane

2014

2014 Information Technology Based Higher Education and Training (ITHET)

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This paper presents three simple applications to let students work with data generated from their mobile phones. The applications can be used as demonstrations for courses in e.g. data mining, business intelligence, and development of digital and web services. One application is entirely web based, the other two require a technology investment of about 200 USD, but the devices may also be an asset for other research/demonstration purposes.

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“…For each situation, the system chooses the most appropriate interaction mechanisms for informing the user about a new relevant content downloaded. This adaptation is carried out by the content visualization module automatically using the interaction adaptation mechanisms presented in [45]. In a nutshell, this module receives the information of the downloaded content, chooses appropriate interaction resources (e.g., dialog, sound), and presents them to the user.…”

Section: Client Sidementioning

confidence: 99%

“…It is worth noticing that only the relevant content downloaded is notified to the user. The specific operations that follow the reconfiguration engine to calculate the appropriate interaction resources are described in [45]. …”

Section: Fig 11 Elements Of the Content Visualization Modulementioning

confidence: 99%

A personalized system for scalable distribution of multimedia content in multicast wireless networks

Fez

Gil

Fons

et al. 2014

Multimed Tools Appl

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Abstract. This paper presents a novel architecture for scalable multimedia content delivery over wireless networks. The architecture takes into account both the user preferences and context in order to provide personalized contents to each user. In this way, third-party applications filter the most appropriate contents for each client in each situation. One of the key characteristics of the proposal is the scalability, which is provided, apart from the use of filtering techniques, through the transmission in multicast networks. In this sense, content delivery is carried out by means of the FLUTE (File Delivery over Unidirectional Transport) protocol, which provides reliability in unidirectional environments through different mechanisms such as AL-FEC (Application Layer -Forward Error Correction) codes, used in this paper. Another key characteristic is the context-awareness and personalization of content delivery, which is provided by means of context information, user profiles, and adaptation. The system proposed is validated through several empirical studies. Specifically, the paper presents evaluations of two types that collect objective and subjective measures. The first evaluate the efficiency of the transmission protocol, analyzing how the use of appropriate transmission parameters reduces the download time (and thus increasing the Quality of Experience), which can be minimized by using caching techniques. On the other hand, the subjective measures present a study about the user experience after testing the application and analyze the accuracy of the filtering process/strategy. Results show that using AL-FEC mechanisms produces download times until four times lower than when no protection is used. Also, results prove that there is a code rate that minimizes the download time depending on the losses and that, in general, code rates 0.7 and 0.9 provide good download times for a wide range of losses. On the other hand, subjective measures indicate a high user satisfaction (more than 80%) and a relevant degree of accuracy of the content adaption.

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Personalization for unobtrusive service interaction

Cited by 21 publications

References 38 publications

Self-adaptive unobtrusive interactions of mobile computing systems

Self-adaptive unobtrusive interactions of mobile computing systems

Reality mining in the classroom

A personalized system for scalable distribution of multimedia content in multicast wireless networks

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