Federica Cena

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.

User identification for cross-system personalisation

Currently, there is an increasing demand for user-adaptive systems for various purposes in many different domains. Typically, personalisation in information systems occurs separately within each system. The recent trends in user modeling rely on cross-system personalisation, i.e., the opportunity to share information across multiple information systems in order to improve user adaptation. Cooperation among systems in order to exchange user model knowledge is a complex task. This paper addresses a key challenge for cross-system personalisation which is often taken as a starting assumption, i.e., user identification. In this paper, we describe the conceptualization and implementation of a framework that provides a common base for user identification for cross-system personalisation among web-based user-adaptive systems. However, the framework can be easily adopted in different working environments and for different purposes. The framework represents a hybrid approach which draws parallels both from centralized and decentralized solutions for user modeling. To perform user identification, we propose to exploit a set of identification properties that are combined using an identification algorithm.

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.

Self-monitoring and Technology: Challenges and Open Issues in Personal Informatics

Personal Informatics (PI), also known as Quantified Self (QS), is a school of thought which aims to use technology for acquiring and collecting data on different aspects of the daily lives of people. These data can be internal states (such as mood or glucose level in the blood) or indicators of performance (such as the kilometers run). Some research was conducted in order to discover the problems related to the usage of PI tools, although none investigated how common users use these tools for tracking their behavior. The goal of this paper is to provide some insights about challenges and open issues regarding the usage of PI tools from the point of view of a common user. To this aim, we provide a theoretical background of personal informatics and a brief review on the previous studies that have investigated the usage pattern of PI tools.

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.

Web 3.0: merging semantic web with social web

Federica Cena Department of Computer Science University of Turin Corso Svizzera 185, 10149 Torino, Italy +39 011 6706711 cena@di.unito.it Rosta Farzan Intelligent Systems Program University of Pittsburgh 5108 Sennott Square Building, 210 S. Bouquet St.,Pittsburgh, USA 412 624-5757 rosta@cs.pitt.edu Pasquale Lops Department of Computer Science University of Bari Via E. Orabona, 4 70126 Bari, Italy +39 080 5442276

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.

Propagating User Interests in Ontology-Based User Model

In this paper we address the problem of propagating user interests in ontology-based user models. Our ontology-based user model (OBUM) is devised as an overlay over the domain ontology. Using ontologies as the basis of the user profile allows the initial user behavior to be matched with existing concepts in the domain ontology. Such ontological approach to user profiling has been proven successful in addressing the cold-start problem in recommender systems, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the ontological structure of the domain. The main contribution of the paper is the novel algorithm for propagation of user interests which takes into account i) the ontological structure of the domain and, in particular, the level at which each domain item is found in the ontology; ii) the type of feedback provided by the user, and iii) the amount of past feedback provided for a certain domain object.