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Adaptive Content Presentation for the Web

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Adaptive Content Presentation for the Web
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  P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 409    –    432, 2007. © Springer-Verlag Berlin Heidelberg 2007 13   Adaptive Content Presentation for the Web Andrea Bunt, Giuseppe Carenini, and Cristina Conati Department of Computer Science University of British Columbia {bunt, carenini, conati}@cs.ubc.ca Abstract.  In this chapter we describe techniques for adaptive presentation of content on the Web. We first describe techniques to select and structure the content deemed to be most relevant for the current user in the current interac-tion context. We then illustrate approaches that deal with the problem of how to adaptively deliver this content. 13.1 Introduction Previous chapters in this book have described types of adaptation for Web-based systems that include adaptive navigation support (see Chapter 8 of this book [8]), adaptive search (see Chapter 6 of this book [39]) and personalized recommendation of items of interest (see Chapters 9 [47], 10 [42], 11 [49], and 12 [9] of this book). In this chapter, we will focus on an additional type of adaptation widely known as adap-tive presentation of content  : how to present Web-based content in a manner that best suits individual users’ needs. This type of adaptation involves determining, based on the user and context, what information the system should present and how the infor-mation should be organized and displayed. While adaptive presentation of content can serve many purposes, as we will demonstrate throughout the chapter, it can also com- plement several of the adaptation types discussed in previous chapters. For instance, the content of Web pages pointed to by a tailored link in a system that provides adap-tive navigation support (Chapter 8 of this book [8]), or returned by adaptive search (Chapter 6 of this book [39]), can be modified to highlight the parts that are more interesting for the current user. Similarly, the description of the items returned by a recommender system (see Chapters 9 [47], 10 [42], 11 [49], and 12 [9] of this book) and can be adapted to play up the items’ features that are more relevant to the user’s needs, or changed to be more suitable to the user’s level of familiarity with the items. The focus of this chapter will be on computational techniques necessary to provide the user with a tailored presentation of content, rather than implementation details and technologies. Also, the chapter is not limited to techniques currently used in adaptive Web-based applications. It aims to suggest areas of future research by discussing alternative approaches that have a strong potential to augment the set of existing tech-niques for adaptive presentation on the Web.  410 A. Bunt, G. Carenini, and C. Conati The process of adapting content to specific user needs comprises two sub proc-esses: content adaptation  and  presentation . Content adaptation involves deciding what content is most relevant to the current user and how to structure this content in a coherent way, before  presenting it to the user. The second sub process of content  presentation  involves deciding how to most effectively adapt the presentation of the selected content to the user. The chapter is structured as follows. In section 13.2, we address techniques for content adaptation. Although traditionally these techniques required the existence of  pre-crafted versions of the relevant content, new techniques are emerging which can automatically adapt content from abstract knowledge sources. Given that the latter lead to greater flexibility and robustness, our discussion focuses on these. In section 13.3, we discuss techniques for content presentation. We first introduce techniques that deal with the problem of how to present this content so that user focus/attention is drawn to the most relevant information (possibly defined by using any of the tech-niques described in section 13.2) while still preserving the contextual information that can often be provided by content of secondary importance. We then discuss tech-niques to decide which media/modality to use to best convey the selected content. 13.2 Techniques for Content Adaptation Content adaptation involves identifying the content most relevant to a given user and context (jointly referred to as the interaction context), as well as how this content should be organized. Relevant properties of the interaction context can include the user’s preferences, interests, and expertise, as well as the presentation goals. Content adaptation of Web pages can be characterized along the following key dimension: the nature of the content provided as input. Along this dimension, we first briefly describe two rather simple approaches in which adaptation is achieved by selecting appropriate canned pages or page fragments. These approaches are referred to in the literature as  page  and  fragment variants  respectively, and they have been extensively discussed in  previous surveys (e.g., [32]). After a brief description of page and fragment variants, we provide an in-depth discussion of more sophisticated approaches to content adap-tation in which the input is abstract information, since to the best of our knowledge, these approaches have never been covered in detail in any previous survey on adap-tive hypermedia. 13.2.1 Approaches Based on Page and Fragment Variants The simplest form of content adaptation is the page-variant approach [32]. Here, the input of the adaptation process consists of different versions of each page that is to be adapted along with a model of the interaction context. These versions have to be writ-ten in advance. At runtime, the adaptation mechanism selects and presents the page version that is most appropriate to the current interaction context. Clearly, this ap- proach does not scale up to complex adaptation. If several aspects of the page must be adapted in many different ways, an unmanageably large number of variants need to be written. Nevertheless, in some domains, where only high-level adaptation is needed, this approach has been effectively applied. For instance, in the ORIMUHS system   13 Adaptive Content Presentation for the Web 411 [14] page variants are applied to support user interaction in two complex software systems: a CAD modeler and a medical application. Page variants are also applied in the KBS Hyperbook system [24] to develop educational courseware on Java pro-gramming. Moving up in the ladder of adaptation complexity, we have the fragment-variant approach. In this approach, the adaptation is performed at a finer level of granularity. More specifically, the page presented to the user is not selected from a pool of fixed  pages. Rather, it is constructed by selecting and combining an appropriate set of fragments, where each fragment typically corresponds to a self-contained information element, such as a text paragraph or a picture. As with the page-variant approach, these fragments are written in advance. Two common strategies for fragment variants are: optional fragments  and altering fragments . In optional fragments, a page is speci-fied as a set of fragments, where each fragment is associated with a set of applicabil-ity conditions. At runtime, the page is generated by selecting only those fragments whose conditions are satisfied in the current interaction context. For instance in [16], different optional fragments are selected depending on the user’s knowledge, interests and abilities. Altering fragments are rather different from optional fragments. In alter-ing fragments, a page is specified as a set of constituents, and for each constituent there is a corresponding set of fragments. At runtime, the page is created by selecting for each constituent the fragment that is most appropriate in the current interaction context. Altering fragments are applied, for instance, in the AHA system [13], in which different presentations of the same entity can be selected depending on whether the target user has the necessary background knowledge .  In general, a noticeable disadvantage of fragment variants compared to page vari-ants is that the selection and assembly of a suitable set of fragments may involve a substantial overhead at runtime. Furthermore, it may sometimes be difficult to com- bine the set of independently selected fragments into a coherent whole. On the other hand, the key advantage of this approach is that, once a set of fragments and their applicability conditions have been written, a large number of pages can be automati-cally generated to cover a corresponding large number of interaction contexts. For  pointers to specific techniques to implement the fragment-variant approach the reader should refer to [32].  Note that because in the two approaches above the units of content adaptation are either whole pages or predefined page components, the two sub processes of content adaptation and presentation actually coincide. That is, the decision of what content is most relevant to the user (i.e. the page to be displayed) uniquely identifies what will  be presented to the user. On the one hand, this simplifies and speeds up the complete adaptation process. On the other hand, it reduces flexibility because it eliminates the  possibility to further tailor the information through adaptive presentation techniques once the first level of adaptive content presentation, content selection, has been achieved, as we will see in section 13.3. 13.2.2 Approaches Based on Abstract Information Although many adaptive Web systems have been designed in recent years by relying only on page or fragment variants, in this section we describe more sophisticated adaptation techniques that allow a system to reason about the input content and the  412 A. Bunt, G. Carenini, and C. Conati interaction context, both of which are expressed in more abstract terms. These tech-niques permit the adaptation to be more flexible, robust and scalable. Notice that part of the research on sophisticated content adaptation has been developed in the field of  Natural Language Generation (NLG) [43], which investigates how natural language text can be generated from abstract non-linguistic information. Sophisticated content adaptation, also called tailoring in NLG, requires an abstract representation of the domain from which the content is selected, as well as the fea-tures of the interaction context to which the content is tailored. Several formalisms have been used in the literature, including: •   Traditional Knowledge Bases  [46] expressing domain entities and relationships  between them. For instance, one application of the ILEX system [40] generates tai-lored jewel labels by relying on a large object-centered knowledge base about jew-elers, materials, designers, etc. This knowledge base includes both abstract propo-sitions, such as the fact that a necklace is a jewel, and specific propositions, such as the fact that a particular jewel was made in Birmingham in 1905. •    Bayesian Networks  [46] expressing probabilistic relationships between random variables representing the domain. For instance, one application of the NAG sys-tem [33],[55] generates arguments about the expected rate of a researcher’s future  publications by relying on a Bayesian Network. This network specifies probabilis-tic relationships between the publication rate of a researcher and the factors that in-fluence it, such as the strength of the institution from which the researcher gradu-ated (e.g., the stronger the institution, the higher the likelihood of a high publica-tion rate). •    Preference Models  [46] expressing the user’s preferences about different aspects of the domain. For instance, one application of the GEA system [10] generates user-tailored arguments on whether the user will like/dislike a given house by relying on a model specifying what aspects of a house the user cares most about (e.g., loca-tion, amenities). The PRACMA system [29] also employs a model of user prefer-ences to tailor its description of an individual recommended item (e.g., a car) by focusing on the aspect (e.g., price) that will have the largest impact on the user’s overall evaluation of that item. Depending on the application, the same or different formalisms can be used to represent the domain model and the interaction context. For instance, in NAG [55], a system for generating factual arguments (claiming that something is or is not the case), both the domain and user model are represented as Bayesian Networks. Similarly, in HYLITE+ [5], a system for generating adaptive hypertext encyclopedia-style explanations, both the domain and the user models are expressed as traditional knowledge bases, more precisely as conceptual graphs [50]. In contrast, in GEA, a system for generating evaluative argu-ments (claiming that something is good vs. bad), the domain model is represented as a traditional knowledge base while the user model is expressed as a value tree [46], which is a preference model commonly used in decision theory. The process of sophisticated content adaptation involves the two conceptually dis-tinct phases of content selection/determination  and content structuring  , also jointly referred to as content planning. Although we will describe them separately to simplify the presentation, it should be noted that content selection and structuring are often implemented as one single process that simultaneously performs both phases [43].   13 Adaptive Content Presentation for the Web 413 Content Selection.  During content selection, a subset of the domain knowledge is identified as relevant for the current user and situation. Strategies for content selection rely on domain-specific knowledge to different degrees. For instance, the content selection strategy used in STOP, a system for generating smoking cessation letters, is quite domain specific as it refers to psychological knowledge about addictive  behavior and smoking [44]. In contrast, the content selection strategy used by the GEA system does not rely on any domain-specific knowledge (as we will see later in this section) and can be therefore applied in any domain [10]. Because of their generality, in this section we focus on strategies that are primarily domain-independent. For a discussion of more domain-specific strategies and in particular of how they can be acquired, the reader should refer to [43].   In practice, most domain-independent strategies for content selection compute a measure of relevance for each content element (i.e., fact) and then use this measure to select an appropriate subset of the available content. Content adaptation is achieved by having this measure of relevance take into account features of the cur-rent user and context. For illustration, let's consider three systems that provide a representative overview of how the measure of relevance can be computed and how it can be used for content selection. The Intelligent Labeling Explorer (ILEX) . We start with ILEX [40], a system for generating contextually-relevant hypertext descriptions of objects (e.g., museum artifacts, computer components). In ILEX, the measure of relevance for content selection combines a measure of structural relevance of a knowledge element/fact with its intrinsic score. Structural relevance takes into account the structure of the domain knowledge base - a semantic net. More specifically, structural relevance is computed starting from the focal entity (i.e., the entity being described) by consid-ering two basic heuristics: (i) information becomes less relevant the more distant it is from the focal object, in terms of semantic links; (ii) different semantic link types (e.g., GENERALISE) maintain relevance to different degrees. The intrinsic score of a knowledge element combines numerical estimates of three factors: (i) the poten-tial interest of the information to the current user, (ii) the importance of the infor-mation to the system's informational goals and (iii) to what degree the user may already know this information. Once the two measures of structural relevance and intrinsic score have been computed, they are combined in a single measure of rele-vance by straight multiplication. In ILEX, the content selection strategy is then to return the n  most relevant knowl-edge elements. However, if the selection process based on relevance cannot find a sufficient number of knowledge elements, additional content selection routines are activated. For instance, one technique applied by ILEX is to identify an entity which is sufficiently similar to the focal entity, so that an interesting comparison between the two can be also selected for presentation. In general, when the goal of a content selec-tion component is to return a fixed amount of content, it may be necessary to supple-ment the main selection strategy with a set of ancillary strategies.
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