Myriam Ribiere

A Viewpoint Model for Cooperative Building of an Ontology

This paper presents the C-VISTA model enabling viewpoint representation for improving knowledge modelling from several experts: this model can thus be used for cooperative building of an ontology. We describe the C-VISTA model in the framework of conceptual graph formalism.

Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery

This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile consists of two separate models, that is, the long-term interests are stored in a skeleton profile on the server and the short-term interests in a detailed profile in the handset. The user profile enables a high-level filtering of available news content on the server, followed by matching of detailed user preferences in the handset. The highest rated items are recommended to the user, by employing an efficient ranking process. The paper focuses on a two-level learning process, which is employed on the client side in order to automatically update both user profile models. It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. The systems learning performance has been systematically evaluated based on data collected from regular system users.

Distributed User Modeling for Personalized News Delivery in Mobile Devices

This paper presents a distributed client-server architecture for the personalized delivery of textual news content to mobile users. The user profile is distributed across client and server, enabling a high-level filtering of available content on the server, followed by matching of detailed user preferences on the handset. The high-level user preferences are stored in a skeleton profile on the server, and the low- level preferences in a detailed user profile on the handset. A learning process for the detailed user profile is employed on the handset exploiting the implicit and explicit user feedback. The systems learning performance has been evaluated based on data collected from regular system users.

Rich service description for a smarter lifestyle

A generally portrayed mobile society vision is captured by the idea of Intelligent Personal mobile Lifestyle Assistants that takes on many forms, they help you find your way around the Internet, they might be mobile, they may be socially intelligent and may even communicate emotional responses when you interact with them using learning techniques to filter and match just what you want when you want it. This brings a promise of a better lifestyle supported through efficient and personalised electronic assistants, which brings just what you need when you need it. Imagine a time when people receive their intelligent Personal mobile lifestyle assistant. Perhaps there will be a legal age before you are able to officially use your electronic assistant. Just like learning to drive a car. Different countries will have different legislations about when, how, codes of practice, legal fines, storage costs etc. In order to support this interaction medium requires rich distributed service framework that supports many different aspects of knowledge content for personalization. Here we make an emphasis on lifestyle as the electronic society becomes part of many peoples way of life for managing business, services and social opportunities.In this paper we focus on the developments and requirements to provide a core definition and support of service description in order to provide an intelligent middleware that is the backbone of enabling the mobile lifestyle we envision. We draw upon the various research activities that contribute to the development of rich service descriptions and the opportunity for aggregation of services in a dynamic environment that can support context-aware needs of m-lifestyle. We extend the current architectures used to enable a richer service description for a smarter lifestyle.