Francesca Carmagnola

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.

An Evidence-Based Approach to Handle Semantic Heterogeneity in Interoperable Distributed User Models

Nowadays, the idea of personalization is regarded as crucial in many areas. This requires quick and robust approaches for developing reliable user models. The next generation user models will be distributed (segments of the user model will be stored by different applications) and interoperable (systems will be able to exchange and use user model fractions to enrich user experiences). We propose a new approach to deal with one of the key challenges of interoperable distributed user models - semantic heterogeneity. The paper presents algorithms to automate the user model exchange across applications based on evidential reasoning and advances in the Semantic Web.

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.

RSS-Based Interoperability for User Adaptive Systems

This paper presents an approach to exploit widely used tag annotations to address two important issues in user-adaptive systems: the cold-start problem and the integration of distributed user models. The paper provides an example of re-use of user interaction data (tags) generated by one application into another one in similar domains for providing cross-system recommendations.

User modeling in the social web

This paper presents the idea to reason over users tags to define and enrich the user model. We apply our approach to an adaptive web-based and multi-device social recommender system: iCITY, which exploits a tag-based user model, enriched from the information derived from the tags inserted in the system by users, and filled also with the tags the user has already exploited in other social web sites. Moreover, we propose an architecture to enable the iCITY tag-based user model be exported and shared with other social applications in a semantic enhanced way. Finally, we propose the sharing of the user profile, together with the list of tags, in a shared syntax (such as RDF(S), OWL, RSS).

Handling Semantic Heterogeneity in Interoperable Distributed User Models

Due to the overspread of user adaptive systems user data are collected and processed in diverse settings and from different platforms. The computational effort to extract user models is commonly repeated across applications and domains, mainly due to lack of interoperability and synchronization among user-adaptive systems. One way of achieving a complete picture of a users experience is to allow systems to share user data to obtain maximum leverage and reuse of information. We address this process as user model interoperability. One of the major challenge to user models interoperability is handling semantic heterogeneity. The paper proposes a new approach for user model interoperability which deals with the semantic heterogeneity of user models and automates the user model exchange across applications.