Genetic testing with primary prevention and moral hazard

We develop a model where a free genetic test reveals whether the individual tested has a low or high probability of developing a disease. A costly prevention effort allows high-risk agents to decrease the probability of developing the disease. Agents are not obliged to take the test, but must disclose its results to insurers. Insurers offer separating contracts which take into account the individual risk, so that taking the test is associated to a discrimination risk. We study the individual decisions to take the test and to undertake the prevention effort as a function of the effort cost and of its e¢ ciency. We obtain that, if effort is observable by insurers, agents undertake the test only if the effort cost is neither too large nor too low. If the effort cost is not observable by insurers, they face a moral hazard problem which induces them to under-provide insurance. We obtain the counterintuitive result that moral hazard increases the value of the test if the effort cost is low enough. Also, agents may perform the test for lower levels of prevention e¢ ciency when effort is not observable

The self-Googling phenomenon : investigating the performance of personalized information resources

This paper investigates self–Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self–Googling visible in the usage of search engines; is any significant difference measurable between queries related to self–Googling and generic search queries; to what extent do self–Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self–Googling and present the results from a 14–month online experiment using Google search engine usage data.

Personalized recommender systems integrating social tags and item taxonomy

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

De la reconstruction a l’augmentation du corps humain en medecine restaurative et en cybernetique

Aux confluences historiques et conceptuelles de la modernité, de la technologie, et de l’« humain », les textes de notre corpus négocient et interrogent de façon critique les possibilités matérielles et symboliques de la prothèse, ses aspects phénoménologiques et spéculatifs : du côté subjectiviste et conceptualiste avec une philosophie de la conscience, avec Merleau-Ponty ; et de l’autre avec les épistémologues du corps et historiens de la connaissance Canguilhem et Foucault. Le trope prometteur de la prothèse impacte sur les formations discursives et non-discursives concernant la reconstruction des corps, là où la technologie devient le corrélat de l’identité. La technologie s’humanise au contact de l’homme, et, en révélant une hybridité supérieure, elle phagocyte l’humain du même coup. Ce travail de sociologie des sciences (Latour, 1989), ou encore d’anthropologie des sciences (Hakken, 2001) ou d’anthropologie bioculturelle (Andrieu, 1993; Andrieu, 2006; Andrieu, 2007a) se propose en tant qu’exemple de la contribution potentielle que l’anthropologie biologique et culturelle peut rendre à la médecine reconstructrice et que la médecine reconstructrice peut rendre à la plastique de l’homme ; l’anthropologie biologique nous concerne dans la transformation biologique du corps humain, par l’outil de la technologie, tant dans son histoire de la reconstruction mécanique et plastique, que dans son projet d’augmentation bionique. Nous établirons une continuité archéologique, d’une terminologie foucaldienne, entre les deux pratiques. Nous questionnons les postulats au sujet des relations nature/culture, biologie/contexte social, et nous présentons une approche définitionnelle de la technologie, pierre angulaire de notre travail théorique. Le trope de la technologie, en tant qu’outil adaptatif de la culture au service de la nature, opère un glissement sémantique en se plaçant au service d’une biologie à améliorer. Une des clés de notre recherche sur l’augmentation des fonctions et de l’esthétique du corps humain réside dans la redéfinition même de ces relations ; et dans l’impact de l’interpénétration entre réalité et imaginaire dans la construction de l’objet scientifique, dans la transformation du corps humain. Afin de cerner les enjeux du discours au sujet de l’« autoévolution » des corps, les théories évolutionnistes sont abordées, bien que ne représentant pas notre spécialité. Dans le cadre de l’autoévolution, et de l’augmentation bionique de l’homme, la somation culturelle du corps s’exerce par l’usage des biotechnologies, en rupture épistémologique de la pensée darwinienne, bien que l’acte d’hybridation évolutionnaire soit toujours inscrit dans un dessein de maximisation bionique/génétique du corps humain. Nous explorons les courants de la pensée cybernétique dans leurs actions de transformation biologique du corps humain, de la performativité des mutilations. Ainsi technologie et techniques apparaissent-elles indissociables de la science, et de son constructionnisme social.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Learning personalized tag ontology from user tagging information

The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved.

Can we deliver both population-based and personalized approaches to cancer care?

Responding to the individual needs of the person affected by cancer is a fundamental tenet of nursing care. The evidence base to enable highly personalized approaches to the way we provide care has grown enormously in recent years. Today, we have a much better understanding of the mechanisms underpinning health needs of people with cancer, as well as the wide range of environmental, sociocultural, psychological, and biological influences on these needs. This growing evidence base enables us to better target and tailor interventions in increasingly sophisticated ways.