L’immense quantité d'information générée et gérée au quotidien par les systèmes d'information et leurs utilisateurs conduit inéluctablement à la problématique de surcharge d'information. Dans ce contexte, les systèmes de recommandation traditionnels fournissent des informations pertinentes aux utilisateurs. Néanmoins, avec la propagation récente des dispositifs mobiles (smartphones et tablettes), nous constatons une migration progressive des utilisateurs vers la manipulation d'environnements pervasifs. Le problème avec les approches de recommandation traditionnelles est qu'elles n'utilisent pas toute l'information disponible pour produire des recommandations. Davantage d’informations contextuelles pourraient être utilisées dans le processus de recommandation pour aboutir à des recommandations plus précises. Les systèmes de recommandation sensibles au contexte (CARS) combinent les caractéristiques des systèmes sensibles au contexte et des systèmes de recommandation afin de fournir des informations personnalisées aux utilisateurs dans des environnements ubiquitaires. Dans cette perspective où tout ce qui concerne l'utilisateur est dynamique, les contenus qu’il manipule et son environnement, deux questions principales doivent être adressées : i) Comment prendre en compte l'évolution des contenus de l’utilisateur? et ii) Comment éviter d’être intrusif, en particulier dans des situations critiques? En réponse à ces questions, nous avons développé un système de recommandation dynamique et sensible au risque appelé DRARS (Dynamic Risk-Aware Recommender System), qui modélise la recommandation sensible au contexte comme un problème de bandit. Ce système combine une technique de filtrage basée sur le contenu et un algorithme de bandit contextuel. Nous avons montré que DRARS améliore la stratégie de l'algorithme UCB (Upper Confidence Bound), le meilleur algorithme actuellement disponible, en calculant la valeur d'exploration la plus optimale pour maintenir un bon compromis entre exploration et exploitation basé sur le niveau de risque de la situation courante de l'utilisateur. Nous avons mené des expériences dans un contexte industriel avec des données réelles et des utilisateurs réels et nous avons montré que la prise en compte du niveau de risque de la situation de l'utilisateur augmentait significativement la performance du système de recommandation / The vast amount of information generated and maintained everyday by information systems and their users leads to the increasingly important concern of overload information. In this context, traditional recommender systems provide relevant information to the users. Nevertheless, with the recent dissemination of mobile devices (smartphones and tablets), there is a gradual user migration to the use of pervasive computing environments. The problem with the traditional recommendation approaches is that they do not utilize all available information for producing recommendations. More contextual parameters could be used in the recommendation process to result in more accurate recommendations. Context-Aware Recommender Systems (CARS) combine characteristics from context-aware systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. In this perspective where everything about the user is dynamic, his/her content and his/her environment, two main issues have to be addressed: i) How to consider content evolution? and ii) How to avoid disturbing the user in risky situations?. In response to these problems, we have developed a dynamic risk sensitive recommendation system called DRARS (Dynamic Risk-Aware Recommender System), which model the context-aware recommendation as a bandit problem. This system combines a content-based technique and a contextual bandit algorithm. We have shown that DRARS improves the Upper Confidence Bound (UCB) policy, the currently available best algorithm, by calculating the most optimal exploration value to maintain a trade-off between exploration and exploitation based on the risk level of the current user's situation. We conducted experiments in an industrial context with real data and real users and we have shown that taking into account the risk level of users' situations significantly increases the performance of the recommender system

Research in risk awareness has been relatively neglected in the health informatics literature, which tends largely to examine project managers’ perspectives of risk awareness; very few studies explicitly address the perspectives held by senior executives such as directors. Another limitation evident in the current risk literature is that studies are often based on American data and/or they are restricted to American culture. Both factors highlight the need to examine how senior executives (i.e., directors) who oversee or direct eHealth projects in Canada perceive risk awareness. This research explores and discusses the perspectives of risk awareness (i.e., identification, analysis, and prioritization) held by directors and project managers who implement Canadian eHealth projects. Semi-structured interviews with nine directors and project managers uncovered six key distinctions in these two groups’ awareness of risk. First, all project managers valued transparency over anonymity, whereas directors believed that an anonymous reporting system for communicating risks had merit. Secondly, most directors emphasized the importance of evidence-based planning and decision making when balancing risks and opportunities, an aspect none of the project managers voiced. Thirdly, while project managers noted that the level of risk tolerance may evolve from being risk-averse to risk-neutral, directors believed that risk tolerance evolved toward risk-seeking. Directors also noted the importance of employing risk officers, a view that was not shared by project managers. Directors also believed the risk of too little end-user engagement and change management was the most important risk, whereas project managers ranked it as the least important. Finally, when directors and project managers were asked to identify and define the root cause(s) of eHealth risks, directors identified the complexity of health care industry, while project managers attributed it to political pressure and a lack of resources where eHealth projects are concerned. This research proposes that the varied perspectives of risk awareness held by directors and project managers must be considered and integrated to properly align expectations and build partnerships for successful eHealth project outcomes. Understanding risk awareness offers a means to systematically identify and analyze the complex nature of eHealth projects by embracing uncertainties, thereby enabling forward thinking (i.e., staying one step ahead of risks) and the ability to prevent avoidable risks and seize opportunities. / Graduate / 0723 / 0489 / 0454 / randbpark@gmail.com