Ilaria Torre

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.

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.

Adaptive systems in the era of the semantic and social web, a survey

In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.

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.

Personalized and Adaptive Services on Board a Car: An Application for Tourist Information

Personalization and adaptation techniques are an interesting opportunity to design new services on-board vehicles. In this context, in fact, the need of an individual user to receive the “right” service at the “right” time and in the “right” way is more critical than in other cases, where personalization and adaptation already showed interesting advantages. At the same time, this context of application can provide new interesting insights for user modeling and adaptation. In the paper we present an architecture for providing personalized services on-board vehicles and we discuss an application to the case of tourist information. We focus on the choices we made to design an on-board system which was as less intrusive and distracting as possible and that could adapt its recommendations, the way it presents them and its own behavior to the users preferences/interests and to the context of interaction (especially the driving conditions).

User data distributed on the social web: how to identify users on different social systems and collecting data about them

This paper presents an approach to uniquely identify users and to retrieve their data distributed in profiles stored in different systems. The objective is exploiting the public user data available in the Web and especially in social networks. The approach does not require the implementation of specific protocols and the provision of authentication data. The evaluation provides good results that encourage us in carrying on the extension of the project. The extension we are working on is aimed at aggregating, using heuristic techniques, the data stored in the retrieved profiles and at inferring new data about the user.

Semantic Web and Internet of Things Supporting Enhanced Learning

This paper outlines possible evolution trends of e-learning, supported by most recent advancements in the World Wide Web. Specifically, we consider a situation in which the Semantic Web technology and tools are widely adopted, and fully integrated within a context of applications exploiting the Internet of Things paradigm. Such a scenario will dramatically impact on learning activities, as well as on teaching strategies and instructional design methodology. In particular, the models characterized by learning pervasiveness and interactivity will be greatly empowered.

Adaptation and Personalization on Board Cars: A Framework and Its Application to Tourist Services

In this paper we analyse the goals and problems that should be taken into account when designing adaptive/personalized services that must run on-board vehicles. This is, in fact, a very interesting and promising area of application where adaptation and personalization can provide unique advantages. We then introduce a framework and a multi-agent architecture for on-board services supporting different forms of user and context modelling and different forms of adaptation and personalization. Finally, to support our claims and framework, we discuss a specific prototype system for on-board tourist services.

Escaping the Big Brother: An empirical study on factors influencing identification and information leakage on the Web

This paper presents a study on factors that may increase the risks of personal information leakage, owing to the possibility of connecting user profiles that are not explicitly linked together. First, we introduce a technique for user identification based on cross-site checking and linking of user attributes. Then, we describe the experimental evaluation of the identification technique both in a real setting and on an online sample, showing its accuracy to discover unknown personal data. Finally, we combine the results on the accuracy of identification with the results of a questionnaire completed by the same subjects who performed the test in the real setting. The aim of the study was to discover possible factors that make users vulnerable to this kind of technique. We found that the number of social networks used, their features and especially the amount of profiles abandoned and forgotten by the user are factors that increase the likelihood of identification and the privacy risks.