Automatic Generation of Tailored Accessible User Interfaces for Ubiquitous Services

Gamecho, Borja; Miñón, Raúl; Aizpurua, Amaia; Cearreta, Idoia; Arrue, Myriam; Garay-Vitoria, Nestor; Abascal, Julio

doi:10.1109/thms.2014.2384452

Cited by 33 publications

(23 citation statements)

References 18 publications

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“…Privacy is an important issue in recommender systems and most techniques are either based on collaborative filtering rating privacy or location privacy. With users moving constantly and by using different contextual variables to receive personalized recommendations, all these data are communicated with services providers (Gamecho et al, 2015;Polatidis, Georgiadis, Pimenidis, & Stiakakis, 2017). Although, in the literature some work towards the protection of context data has been done, more work towards this direction will be necessary (Polatidis, Georgiadis, Pimenidis, & Stiakakis, 2017).…”

Section: How Can User Privacy Be Protected When Context Parameters Armentioning

confidence: 99%

Mobile recommender systems: Identifying the major concepts

Pimenidis

Polatidis

Mouratidis

2018

Journal of Information Science

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This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain. Keywords Mobile recommender systems, Collaborative filtering, Context, PrivacyRecommender systems, depending on the method they employ can be classified in one of the following categories (Bobadilla et al., 2013; Jannach et al., 2010;Shi, Larson, & Hanjalic, 2014;Su & Khoshgoftaar, 2009):Collaborative filtering. These recommenders suggest items to users that other users with a similar rating history have liked in the past.Content-based. These recommenders suggest items to users that are similar to the items that user has liked in the past.Knowledge-based. These recommenders suggest items to users based either on inferences about the preferences of users or by utilizing specific domain knowledge.

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Section: How Can User Privacy Be Protected When Context Parameters Armentioning

confidence: 99%

Mobile recommender systems: Identifying the major concepts

Pimenidis

Polatidis

Mouratidis

2018

Journal of Information Science

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“…For Egoki, we adopted User Interface Markup Language (UIML) (Phanouriou, 2000) because it includes the necessary mechanisms to map different types of resources to each interaction element (pictogram, text, audio, etc.) giving us an effective way to provide multimodality in the user interfaces (Gamecho et al, 2015). The accessible final user interface is automatically generated from that formal description, also taking into account the information about the user's characteristics that is stored in an ontology specifically developed for this system and named EGONTO (see Figure 1).…”

Section: The Egoki Systemmentioning

confidence: 99%

“…By taking these inputs into account, the automatic generation of user interface process selects suitable adaptation rules and the most adequate multimedia resources for the user group and the service to interoperate with. The user interface generation consists of three main phases (Gamecho et al, 2015):…”

Section: The Egoki Systemmentioning

confidence: 99%

“…More detailed information on the Egoki system, such as implementation features, can be found in Gamecho et al (2015)…”

Section: The Egoki Systemmentioning

confidence: 99%

“…In order to provide each user with an accessible interface for ubiquitous services, we developed the Egoki system (Gamecho et al, 2015). Egoki automatically generates modelbased user interfaces.…”

Section: Introductionmentioning

confidence: 99%

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Accessible Ubiquitous Services for Supporting Daily Activities: A Case Study with Young Adults with Intellectual Disabilities

Aizpurua

Miñón

Gamecho³

et al. 2018

International Journal of Human–Computer Interaction

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Ubiquitous environments have considerable potential to provide services supporting daily activities (using public transportation to and from workplace, using ATM machines, selecting and purchasing goods in ticketing or vending machines, etc.) in order to assist people with disabilities. Nevertheless, the ubiquitous service providers generally supply generic user interfaces which are not usually accessible for all potential end users. In this article, a case study to verify the adequacy of the user interfaces automatically generated by the Egoki system for two supporting ubiquitous services adapted to young adults with moderate intellectual disabilities was presented. The task completion times and the level of assistance required by participants when using the interfaces were analyzed. Participants were able to access services through a tablet and successfully complete the tasks, regardless of their level of expertise and familiarity with the service. Moreover, results indicate that their performance and confidence improved with practice, as they required fewer direct verbal and pointer cues to accomplish tasks. By applying observational methods during the experimental sessions, several potential improvements for the automated interface generation process were also detected.

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