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Some Issues Regarding the Design of Adaptive Interface Generation Systems Julio Abascal, Amaia Aizpurua, Idoia Cearreta, Borja Gamecho, Nestor Garay, and Raúl Miñón Informatika Fakultatea. University of the Basque Country/Euskal Herriko Unibertsitatea Manuel Lardizabal 1, 20018 Donostia, Spain {julio.abascal,amaia.aizpurua,idoia.cearreta,borja.gamecho, nestor.garay,raul.minon}@ehu.es Abstract. This paper describes the main issues related to the design of user adaptive interaction systems, in order to discuss their applicability to a specific domain: the automatic generation of accessible user interfaces for people with disabilities who make use of ubiquitous services. Advances in the domain of the accessible Web are especially interesting for this purpose. Nevertheless, even if several procedures are similar, there are specific features that require new approaches, such as the formal specification of the functionality of the interface that will be generated. Keywords: Adaptive Systems, Special Needs, Adaptive User Interfaces, Ubiquitous Computing, Knowledge Modelling.

1 Introduction It is well known that users with sensory, physical or cognitive disabilities have enormous difficulties using devices and applications, and obtaining services through them, because several of them are not accessible. Most frequently the main accessibility barriers lie in the user interfaces, which are habitually designed without taking human diversity into consideration. The most natural way to cope with diversity appears to be adaptation. In the last decades, people working in diverse areas of the Artificial Intelligence field have been working on adaptive systems, hence creating valuable knowledge that can be applied to the design of adaptive user interfaces for people with disabilities. Our laboratory is involved in a research effort to design accessible interfaces for people with disabilities using intelligent machines, such as ATMs, vending machines, information kiosks, home intelligent appliances, etc., in a ubiquitous context. In order to establish the foundations for our system, we reviewed and comment the state of the art in modelling for user adapted interaction in diverse domains and its applicability to the provision of ubiquitous services for people with disabilities. This paper summarises a number of results from our discussions. After this brief introduction, section 2 introduces the principal techniques for knowledge modelling. In section 3 the main adaptation methods are summarized. In the following section reasoning approaches are discussed. Section 5 provides examples of adaptive systems for personalizing the user interface, emphasizing the ubiquitous computing area. The paper ends with some conclusions. C. Stephanidis (Ed.): Universal Access in HCI, Part I, HCII 2011, LNCS 6765, pp. 307–316, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 Knowledge Modelling User modelling has been addressed by an increasing number of research projects. The reason is that knowledge modelling is essential to creating adaptive systems that generate personalized user interfaces. Thus, thanks to the information gathered in user models, it is possible to adapt the content or the presentation to users’ needs, greatly enhancing the quality of user interfaces. To this end, three main phases can be distinguished in order to create a model: (1) the design of the model aims to describe the selected concepts and their relationships; (2) the representation of the model tries to specify the model using a particular modelling technology; and (3) the application of the model, which includes data acquisition in order to fill in the model with real user information. Although this section describes those three phases through examples, it is mainly focused on the first one, since it forms the basis of knowledge modelling. According to the above-mentioned phases, the first task in designing a model is to analyze exhaustively the relevant user characteristics. In addition to user characteristics, context-related concept modelling is also necessary (for instance, information regarding the mobile device, location, etc.) in some domains, such as mobile environments. The selection of context-related concepts generally depends on the application domain of the system. As stated before, some authors only consider users’ features in order to create user profiles. For instance, Jrad et al. [1] focus their work on how features such as user preferences and interests can be modelled, in order to develop tourism-related personalization systems. Another example is the work of Casas et al. [2], who base their model on characteristics related to users’ capabilities and behavior. They argue that this procedure contributes positively to the development of intelligent systems that are able to help elderly users with impairments to overcome their communication barriers. In certain cases, users’ information is gathered and classified into stereotypes [3]. In this way, user interfaces are adapted according to similar characteristics that a group of users has in common. The proposed adaptation is mainly aimed at people with motor impairments. As a global review of models considering only user features, Gauch et al. [4] analyze the most popular techniques for collecting users’ characteristics in order to represent and build user profiles. Other authors compose the user context from more concepts, apart from own user characteristics. For instance, Brusilovsky and Millán [5] provide a wide review of issues related to user models and user modelling approaches applied to the adaptive Web. In addition to features such as the user’s knowledge, interests, goals, background or individual traits, they also discuss modelling the user’s work context including concepts such as the type of platform, user location or user’s affective state. Similar to this last research line, Cearreta and Garay [6] propose the modelling of affective states, including other aspects of the subject, such as physical or cognitive states. Cearreta and Garay refer to the term “subject context” instead of personal context, in order to model aspects regarding both the user and the system. In this way, they model affective interactions between users and systems, taking their context into account. Within this context, apart from the subject’s features, they also include environmental, task, socio-cultural and spatio-temporal context aspects.

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Other authors model similar concepts of the context, using a different classification or terminology. For instance, Göker and Myrhaug [7] structure the context with the following parts: personal, environmental, task, social and spatio-temporal. Another example is the work of Krüger et al. [8], which models the context in the following way: physical, spatial, temporal, activity and situation. Schmidt et al. [9] propose a hierarchical model in which two different contexts are distinguished on the top level: human factors (user, social environment and task) and physical environment (conditions, infrastructure and location). It is clear that finding a consensus for context modelling is not easy. Although many authors use similar concepts for modelling, each of them focuses on the needs of his system to develop and the end users. With regard to the model representation phase, several technologies have been proposed. Some of the most popular technologies to represent the user model or create user profiles are: network-based [10]; term- or keyword-based [11]; mark-up language-based [12]; hierarchies of concepts, based on a taxonomy or thesaurus [13]; more completed concept-based hierarchies using existing ontologies [14]; or ontologies created from scratch [15]. Nowadays, ontology-based techniques are being widely used for representing knowledge. They are usually based on different technologies, such as XML [16], RDF [17] and OWL [18]. Their main objective is to represent concepts of the real world, making it possible to share knowledge among different applications. Moreover, ontologies provide several mechanisms to reuse the domain knowledge or even infer additional information, using reasoning techniques. The use of ontologies can be found in several references related to adaptive systems. For instance, Hérvas and Bravo [19] developed an infrastructure to support information adaptability for users, using ontologies for the management of contextual information. Once the user model is designed and represented, it is ready for the data acquisition phase. In this regard, methods for collecting user information can be divided into two main categories: explicit and implicit [4]. Explicit user information collection methods require user intervention in order to complete the information of the model. For instance, sometimes users are explicitly asked to provide information by filling in web forms. The main downside of this type of method is that it requires users’ willingness and can be disturbing for them. In order to avoid this, implicit methods acquire user data in a transparent way, and thus they do not place a burden on the user. These information-capturing techniques are based on monitoring the user interaction behavior, frequently using data-mining techniques. Some systems have tried hybrid approaches to collect different types of information at diverse stages.

3 Adaptation Methods Apart from building the user model, it is necessary to identify the adaptations that the system will perform to provide users with personalized or adaptive interfaces. There is a wide variety of adaptive methods and techniques that can be classified into three main groups, depending on the adaptation purpose: content, presentation and navigation adaptation [20]. Nonetheless, some methods may fall simultaneously into two categories, since they adapt either content and presentation or navigation and presentation.

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Content adaptation techniques are frequently oriented to providing users with personalized content. There are several approaches, some of which add, delete or modify the content, while others can emphasize or deemphasize it, suggesting the most relevant content according to the user’s interests or needs. According to Knutov et al., adaptive navigation methods are divided into two main categories: “suggesting” and “enforcing” techniques. “Suggesting” techniques color, emphasize or sort the links of a site, keeping all of them. In contrast, “enforcing” techniques delete non-recommended links or add new links. This means that some of the original links might not be present in the adapted user interface. Presentation methods include modification of the layout considering the user’s preferences. The previously mentioned content adaptation techniques, which emphasize and deemphasize parts of the web page, can also be considered as presentation techniques. Moreover, navigation techniques, such as coloring links, can be included in presentation techniques too, if the adaptation affects styles. Figure 1, based on [20], shows a classification of adaptation methods. Not all the adaptive systems require the use of all three main types of adaptive methods mentioned previously. Depending on the purpose and application domain of the adaptive system, the application of some types of techniques would not be required. For instance, adaptive systems corresponding to e-learning domains may focus on adaptive content, rather than applying presentation-related techniques.

4 Reasoning Approaches for Relating Modelling to Adaptation As explained in previous sections, modelling refers to the description of the knowledge regarding the user and his/her interaction context; whereas adaptation techniques refer to the implementation of methods in order to perform adaptations on the user interface. Both information modelling and adaptation techniques are essential components of an adaptive system. In this section, different reasoning mechanisms for relating knowledge models to adaptations are described. This relation aims to provide the system with the knowledge required in order to select the most suitable adaptations for a particular user. There are two main approaches to organizing modelling and adaptation components: User Modelling Shell Systems and User Modelling Servers [21]. In the former case, all user modelling (including content representation and reasoning mechanisms) is performed by the adaptive system. On the contrary, in the latter case the modelling is not part of the adaptive system itself, since the servers are independent from the application. Although both approaches try to separate the user modelling functionality from the adaptive application, they follow different architectures. The server approach has some advantages compared with the shell systems. For example, the servers can easily be integrated into existing systems or environments. In addition, they can serve more than one application at a time. In this way user information can be available to several applications simultaneously. Nonetheless, user modelling servers have some drawbacks, such as the management of distributed user information and support for privacy issues.


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Fig. 1. Classification of the adaptation methods based on Knutov, E., De Bra, P, Pechenizkiy, M. (2008) [20]

With the advent of Ubiquitous Computing, other trends are becoming popular, for instance agent-based user modelling for mobile environments. Regardless of the architecture approach adopted, the information on the model has to be closely related to the adaptive application. Although the relation between the model and the adaptations might sound obvious, it is not trivial and it really plays a key role in every adaptive system. Once the model is created, the complexity of the matching depends on the technology used to represent the model. If the user’s information is represented through a pair of keywords (concept–value), the relation is quite straightforward. For instance, a keyword pair such as (contrast, high) implies that a particular user needs a high contrast on his user interface. Nevertheless, when the model is more complex, reasoning mechanisms are required in order to derive assumptions about the user. These reasoning mechanisms can be classified into different types: deductive reasoning, inductive reasoning and analogical reasoning [22]. Deductive reasoning is applied to infer from the more general to the more specific knowledge. It is used in logic-based user model representation, such as concept formalism. For instance, in adaptive systems that use ontologies to represent the model as a concept hierarchy, the relation can be specified through rules, and later reasoning mechanisms can be used to infer knowledge regarding the user. According to Kobsa et al. [22], one of the main drawbacks of logic-based approaches is the limited support for uncertainty.

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In order to deal with this, Bayesian networks and fuzzy-logic-based approaches make use of probabilities in the user model. In contrast to the deductive approach, inductive reasoning infers assumptions from the specific cases to the general one. It entails keeping track of users’ interaction behavior and deriving a general conclusion. For this purpose, machine learning techniques, such as Nearest-Neighbor or Naive Bayes classification algorithms, can be used. This type of reasoning is often used to infer knowledge about users, such as interests, by analyzing the items rated by users according to their affinity. In regard to analogical reasoning, this approach is based on identifying similarities among users. In this case, rather than analyzing the features of the items that each user is interested in, the system tries to find other users with similar interaction behavior. Based on clustering algorithms similar user profiles can be found and grouped. The Amazon [23] website is an example of an adaptive system that applies some of the above-mentioned reasoning techniques. It recommends products that users may want to buy in a tailored way by considering product ratings. Moreover, it also suggests products by analyzing users’ previous purchases, browsing history and changes in interest of similar user profiles. In this way, suggestions based on the user profile are adapted for each user. In this section we have described several mechanisms to relate modeled information to adaptations, to be performed by the adaptive system. From our experience in developing prototypes for the INREDIS [24] project, rule-based mechanisms were used in the adaptive system to relate the user model to the different adaptation techniques. The user model and the adaptations were represented through an ontology. This allowed us to use predefined rules and a reasoner, in order to identify the most suitable adaptations for a particular user.

5 Adaptive Systems for Personalizing User Interfaces The previous sections describe the main issues regarding the design of an adaptive system. Diverse adaptive systems that apply specific approaches to these issues can be found in the literature. These systems belong to different application domains, such as e-learning, e-commerce or domotics. Moreover, some of them adapt web-based user interfaces, whereas others are standalone approaches. This section includes a number of examples of adaptive systems corresponding to both approaches. One of the first adaptive web system approaches was the AVANTI project [3]. The main objective was to provide hypermedia information to people with special needs by adapting the content, presentation and navigation of web pages to individual users. As mentioned in section 2, Fink et al. modeled users according to different stereotypes. Depending on the stereotype, the system presented optional content and chose the most appropriate information among alternatives. The mPersona architecture [25] is a flexible content personalization system for users with mobile devices. This architecture adapts web-based interfaces by selecting the most suitable content for users, based on their interests and preferences. This adaptive approach is based on mobile agents. The Supple system [26] automatically generates standalone interfaces adapted to a person’s device, tasks, preferences and abilities. Interface generation is presented as a


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discrete optimization problem and it is solved with a branch-and-bound algorithm using constraint propagation. The adaptations are expressed in a cost function that can easily be modified to build new adaptations. Content, navigation and presentation adaptations are considered in the system to generate the interfaces. In the area of the adaptive Web, the common approach is to adapt a previously designed user interface, in order to personalize user interfaces tailored to users’ capacities, skills, access device, task, etc. However, when user interfaces have to be adapted in other scenarios, such as ubiquitous environments, the adaptation process may differ, even though web technologies might be used. In the next subsection the adaptation process in ubiquitous environments will be described. 5.1 User Interface Adaptation in Ubiquitous Environments As mentioned before, adapting user interfaces in ubiquitous environments requires a different approach. In the so-called Ambient Intelligence when a mobile user enters a place where ubiquitous services are provided, his/her mobile device and the local service communicate in a way that is transparent to the user. This is frequently achieved through a wireless network controlled by the appropriate middleware. In this way, it is possible to establish a direct user–service communication. In order to provide services through this type of architecture, each service must be described in an abstract way. When a user accepts or requests a service, a specific instance of the service’s user interface is downloaded into his/her mobile device. This interface should be adapted to the user’s characteristics and his/her context. It should be noted that the final user interface is not predesigned; in contrast, the interface is dynamically adapted from an abstract description of the interaction elements. Therefore, a machine-readable formal specification of its functionality is necessary in order to guide the process of creating the final user interface. For this purpose, User Interface Definition Languages (UIDLs) are widely used [27]. Although the adaptation process of ubiquitous environments is different from that used for web-based adaptive systems, they can use similar adaptation techniques. A number of examples of adaptive systems in ubiquitous environments are presented below. Ubiquitous Interactor [28] addresses the problems of accessing ubiquitous services from distinct devices with different features. The interaction between services and devices is modeled, and customization forms have been implemented in order to adapt the presentation. Another instance is provided by the previously mentioned INREDIS project [29]. Its main goal was to create a framework to provide people with disabilities with access to ubiquitous services. To accomplish this goal, the system transforms an abstract user interface into a final user interface considering user stereotypes that guide the resource selection and page rendering. Finally, one of latest approaches is ViMos [19]. This is an information visualization service that applies context awareness to provide users with adapted information through embedded devices in the environment. In order to present diverse content, pieces of information are represented as user interface widgets. These widgets have several associated techniques of scalability to adapt themselves according to the content to display and the available area in the user interface. ViMos includes a library of widgets in order to display multiple kinds of data (e.g., plain text,

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images, multimedia and formatted documents) by using different visualization techniques and providing adaptive techniques to adjust the visual layout (e.g., zoom, pagination and scrolling). From our point of view after the INREDIS project, the selection of adequate resources and web elements is a key task. People with disabilities must be provided with accessible interfaces, and their ability to interact with them might be compromised if the resources are not appropriate. For example, a hearing-impaired user should be provided with audio transcriptions if the service offers audio elements. Therefore, to build an adapted user interface with adequate resources, the service provider must offer different types of resources. For instance, if the user is illiterate, the system should provide an interface based on images. To this end, the provider of the service should offer these images, in order to make this personalization possible. Additionally, knowledge regarding adaptive techniques should be exploited. It is necessary to provide users with navigation mechanisms adapted to their needs, accessible content and a suitable layout presentation.

6 Conclusion It is generally accepted that some application fields require intelligent adaptive interfaces in order to grant accessibility to applications, devices and services. In this paper we have summarized the most popular techniques used to design user adaptive applications, with special emphasis on the most recent techniques applied in web adaptation. From this exercise we extracted the main guidelines applicable to a different domain: the dynamic generation of adapted user interfaces for a Ubiquitous Computing environment. These ideas have been applied in the design of the first prototype of the Interface Generator of the INREDIS project. From the experience obtained in that project we are currently working on a more advanced adaptive system called EGOKI. Acknowledgments. This work was started within the INREDIS project, founded by the Spanish Industry Ministry and it has been partially supported by the Department of Education, Universities and Research of the Basque Government. In addition, A. Aizpurua, B. Gamecho and R. Miñón enjoy PhD scholarships from the the Research Staff Training Program of the same Department.

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