Cristina Gena

User Modeling and Recommendation Techniques for Personalized Electronic Program Guides

This chapter presents the recommendation techniques applied in Personal Program Guide (PPG). This is a system generating personalized Electronic Program Guides for Digital TV. The PPG manages a user model that stores the estimates of the individual user’s preferences for TV program categories. This model results from the integration of different preference acquisition modules that handle explicit user preferences, stereotypical information about TV viewers, and information about the user’s viewing behavior. The observation of the individual viewing behavior is particularly easy because the PPG runs on the set-top box and is deeply integrated with the TV playing and the video recording services offered by that type of device.

Methods and techniques for the evaluation of user-adaptive systems

This article presents a comprehensive overview of methods and techniques used for the evaluation of user-adaptive systems. It describes the methodologies derived both from the evaluation of human–computer interaction systems and from information retrieval and information filtering systems by giving examples of the application of these methodologies in the user-adaptive systems. The state of the art and the main results in the evaluation of these systems are reported. In particular, empirical evaluation and layered approaches are discussed in detail. Finally, focus on less explored methodologies, such as qualitative approaches (e.g. Grounded Theory), is proposed.

User model interoperability: a survey

Nowadays a large number of user-adaptive systems has been developed. Commonly, the effort to build user models is repeated across applications and domains, due to the lack of interoperability and synchronization among user-adaptive systems. There is a strong need for the next generation of user models to be interoperable, i.e. to be able to exchange user model portions and to use the information that has been exchanged to enrich the user experience. This paper presents an overview of the well-established literature dealing with user model interoperability, discussing the most representative work which has provided valuable solutions to face interoperability issues. Based on a detailed decomposition and a deep analysis of the selected work, we have isolated a set of dimensions characterizing the user model interoperability process along which the work has been classified. Starting from this analysis, the paper presents some open issues and possible future deployments in the area.

Tag-based user modeling for social multi-device adaptive guides

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0’s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.

Usability engineering for the adaptive web

This chapter discusses a usability engineering approach for the design and the evaluation of adaptive web-based systems, focusing on practical issues. A list of methods will be presented, considering a user-centered approach. After having introduced the peculiarities that characterize the evaluation of adaptive web-based systems, the chapter describes the evaluation methodologies following the temporal phases of evaluation, according to a user-centered approach. Three phases are distinguished: requirement phase, preliminary evaluation phase, and final evaluation phase. Moreover, every technique is classified according to a set of parameters that highlight the practical exploitation of that technique. For every phase, the appropriate techniques are described by giving practical examples of their application in the adaptive web. A number of issues that arise when evaluating an adaptive system are described, and potential solutions and workarounds are sketched.

Towards a Tag-Based User Model: How Can User Model Benefit from Tags?

Social tagging is a kind of social annotation by which users label resources, typically web objects, by means of keywords with the goal of sharing, discovering and recovering them. In this paper we investigate the possibility of exploiting the user tagging activity in order to infer knowledge about the user. Up to now the relation between tagging and user modeling seems not to have been investigated in depth. Given the widespread diffusion of web tools for collaborative tagging, it is interesting to understand how user modeling can benefit from this feedback.

The Role of Ontologies in Context-Aware Recommender Systems

This position paper describes the role ontologies can play in Mobile Context-Aware recommender systems. In a Semantic Web vision of recommender systems, the adoption of ontologies for modeling the domain, the context and the adaptation process can contribute to tailor the right information/service to users and thus facilitate the user-system interaction and the system communication with other agents.

Personalized Recommendation of TV Programs

This paper presents the recommendation techniques applied in Personal Program Guide (PPG), a system generating personalized Electronic Program Guides for digital TV. The PPG recommends TV programs by relying on the integration of heterogeneous user modeling techniques.

UbiquiTO: a Multi-Device Adaptive Guide

This paper describes UbiquiTO, an adaptive tourist guide, conceived as a “journey companion” for mobile users in Turin, aimed, for the current prototype, at supporting mobile workers helping them to organize their late afternoon and evening in town. The paper is intended to emphasize the most relevant feature of the system, that is the integration of different adaptation strategies in order to allow high flexibility in terms of device used, localization technology, user preferences and context conditions.

Choice Architecture for Human-Computer Interaction

People in human–computer interaction have learned a great deal abouthow to persuade and influence users of computing technology. Theyhave much less well-founded knowledge about how to help users choosefor themselves. Its time to correct this imbalance. A first step is toorganize the vast amount of relevant knowledge that has been builtup in psychology and related fields in terms of two comprehensive buteasy-to-remember models: The ASPECT model answers the question“How do people make choices?“ by describing six choice patterns thatchoosers apply alternately or in combination, based on Attributes, Socialinfluence, Policies, Experience, Consequences, and Trial and error.The ARCADE model answers the question “How can we help peoplemake better choices?“ by describing six general high-level strategies forsupporting choice: Access information and experience, Represent thechoice situation, Combine and compute, Advise about processing, Designthe domain, and Evaluate on behalf of the chooser. These strategiescan be implemented with straightforward interaction design, butfor each one there are also specifically relevant technologies. Combiningthese two models, we can understand virtually all existing and possibleapproaches to choice support as the application of one or more of theARCADE strategies to one or more of the ASPECT choice patterns.After introducing the idea of choice architecture for human–computerinteraction and the key ideas of the ASPECT and ARCADEmodels, we discuss each of the Aspect patterns in detail and show howthe high-level ARCADE strategies can be applied to it to yield specifictactics. We then apply the two models in the domains of online communitiesand privacy. Most of our examples concern choices about theuse of computing technology, but the models are equally applicable toeveryday choices made with the help of computing technology.