Controlling the Fairness / Accuracy Tradeoff in Recommender Systems

Recommender systems are one of the most pervasive applications of machine learning. They play a pivotal role in helping users find items tailored to their taste. Although these systems intend to assist people in their information needs, they can cause implicit or explicit discrimination against individuals or groups. There are several ways that different biases can creep into recommender systems. Reflection of societal and historical prejudices in datasets and during the data collection process, lack of sufficient data on minority groups, lack of suitable evaluation methods and model designs to detect these biases and lessen the unfairness caused by them are among the many reasons for unfairness in these systems. A system needs to defend against the biases in recommendation output to prevent harm and unfairness. However, integrating the goal of fairness with accuracy in recommender systems is challenging, primarily because of this goal's significant trade-offs with accuracy. Accuracy in recommender systems is the ability of that system to predict users' needs and interests accurately. On the other hand, fairness is a complicated concept with a variety of definitions. To use fairness as an objective, we need to define it based on the application area and the context of a problem. Additionally, we need to specify the fairness concerns of the different stakeholders involved in the recommender systems and the fairness priorities of a system. Any of these aspects might disagree with the goal of accuracy. For example, if fairness for content providers is more exposure to users, increasing it might cause a reduction in accuracy. Therefore, controlling the trade-off between accuracy and fairness becomes essential. Throughout this dissertation, several recommendation models and re-ranking approaches are presented that aim to address this problem using in- and post- processing methods. These approaches show promising results, but it is worth mentioning that they have intrinsic limitations and, therefore, shouldn't be considered ultimate solutions

Examining Consumers’ Selective Information Privacy Disclosure Behaviors in an Organization’s Secure e-Commerce Systems

The study is an examination of the antecedents to the paradoxical changes in the consumers’ intended and actual personal information disclosure behaviors in online transactions or in e-commerce environments. The argument is that a consumer’s information privacy paradox is based on the consumer’s cognitive predisposition. The study adopted the conceptual underpinning inherent in the Privacy Regulation Theory (PRT) and translated them into information privacy context, as the consumer’s desired state of information privacy, information privacy self-interest, information privacy permeability, and information privacy equipoise constructs, to examine the causal relationship among the constructs and between a consumer’s selective personal information disclosure behavior variable. The theoretical model was advanced based on the conceptual framework in PRT and was validated using Structural Equation Modeling. In addition, the study conducted hypothesis testing and factor analysis using Confirmatory Factor Analysis in order to determine the existence of statistical significance and causality. The result indicates that the consumers’ willingness to transact online and disclose their personal information depend largely on the degree of their need signal (self-interest), and to some extent, their awareness and concern of the online merchant’s capacity to collect their personal information, irrespective of their previously declared or undeclared intent to transact and disclose personal information, or despite their desired natural state of information privacy. In other words, the existence of the information privacy paradox stems from the fact that a consumer’s intention to disclose personal information online depends on the person’s natural or desired state of information privacy, whereas the customer’s actual personal information disclosure behavior depends on his or her information privacy equipoise

Modélisation et dérivation de profils utilisateurs à partir de réseaux sociaux : approche à partir de communautés de réseaux k-égocentriques

Dans la plupart des systèmes nécessitant la modélisation de l'utilisateur pour adapter l'information à ses besoins spécifiques, l'utilisateur est représenté avec un profil généralement composé de ses centres d'intérêts. Les centres d'intérêts de l'utilisateur sont construits et enrichis au fil du temps à partir de ses interactions avec le système. De par cette nature évolutive des centres d'intérêts de l'utilisateur, le profil de l'utilisateur ne peut en aucun moment être considéré comme entièrement connu par un système. Cette connaissance partielle du profil de l'utilisateur à tout instant t a pour effet de réduire considérablement les performances des mécanismes d'adaptation de l'information à l'utilisateur lorsque le profil de l'utilisateur ne contient pas (ou contient très peu) les informations nécessaires à leur fonctionnement. Cet inconvénient est particulièrement plus récurrent chez les nouveaux utilisateurs d'un système (instant t=0, problème du démarrage à froid) et chez les utilisateurs peu actifs. Pour répondre à cette problématique, plusieurs travaux ont exploré des sources de données autres que celles produites par l'utilisateur dans le système : utilisateurs au comportement similaire (utilisé dans le filtrage collaboratif) ou données produites par l'utilisateur dans d'autres systèmes (conception de profil utilisateur multi-application et gestion des identités multiples des utilisateurs). Très récemment, avec l'avènement du Web social et l'explosion des réseaux sociaux en ligne, ces derniers sont de plus en plus étudiés comme source externe de données pouvant servir à l'enrichissement du profil de l'utilisateur. Ceci a donné naissance à de nouveaux mécanismes de filtrage social de l'information : systèmes de recherche d'information sociale, systèmes de recommandation sociaux, etc. Les travaux actuels portant sur les mécanismes de filtrage social de l'information démontrent que ce nouveau champ de recherche est très prometteur. Une étude sur les travaux existants nous permet tout de même de noter particulièrement deux faiblesses : d'une part, chacune des approches proposées dans ces travaux reste très spécifique à son domaine d'application (et au mécanisme associé), et d'autre part, ces approches exploitent de manière unilatérale les profils des individus autour de l'utilisateur dans le réseau social. Pour pallier ces deux faiblesses, nos travaux de recherche proposent une démarche méthodique permettant de définir d'une part un modèle social générique de profil de l'utilisateur réutilisable dans plusieurs domaines d'application et par différents mécanismes de filtrage social de l'information, et à proposer d'autre part, une technique permettant de dériver de manière optimale des informations du profil de l'utilisateur à partir de son réseau social. Nous nous appuyons sur des travaux existants en sciences sociales pour proposer une approche d'usage des communautés (plutôt que des individus) autour de l'utilisateur. La portion significative de son réseau social est constituée des individus situés à une distance maximum k de l'utilisateur et des relations entre ces individus (réseau k-égocentrique). A partir de deux évaluations de l'approche proposée, l'une dans le réseau social numérique Facebook, et l'autre dans le réseau de co-auteurs DBLP, nous avons pu démontrer la pertinence de notre approche par rapport aux approches existantes ainsi que l'impact de mesures telles que la centralité de communautés (degré ou proximité par exemple) ou la densité des réseaux k-égocentriques sur la qualité des résultats obtenus. Notre approche ouvre de nombreuses perspectives aux travaux s'intéressant au filtrage social de l'information dans de multiples domaines d'application aussi bien sur le Web (personnalisation de moteurs de recherche, systèmes de recommandation dans le e-commerce, systèmes adaptatifs dans les environnements e-Learning, etc.) que dans les intranets d'entreprise (systèmes d'analyses comportementales dans les réseaux d'abonnés de clients télécoms, détection de comportements anormaux/frauduleux dans les réseaux de clients bancaires, etc.).In most systems that require user modeling to adapt information to each user's specific need, a user is usually represented by a user profile in the form of his interests. These interests are learnt and enriched over time from users interactions with the system. By the evolving nature of user's interests, the user's profile can never be considered fully known by a system. This partial knowledge of the user profile at any time t significantly reduces the performance of adaptive systems, when the user's profile contains no or only some information. This drawback is particularly most recurrent for new users in a system (time t = 0, also called cold start problem) and for less active users. To address this problem, several studies have explored data sources other than those produced by the user in the system: activities of users with similar behavior (e.g. collaborative filtering techniques) or data generated by the user in other systems (e.g., multi-application user's profiles, multiple identities management systems). By the recent advent of Social Web and the explosion of online social networks sites, social networks are more and more studied as an external data source that can be used to enrich users' profiles. This has led to the emergence of new social information filtering techniques (e.g. social information retrieval, social recommender systems). Current studies on social information filtering show that this new research field is very promising. However, much remains to be done to complement and enhance these studies. We particularly address two drawbacks: (i) each existing social information filtering approach is specific in its field scope (and associated mechanisms), (ii) these approaches unilaterally use profiles of individuals around the user in the social network to improve traditional information filtering systems. To overcome these drawbacks in this thesis, we aim at defining a generic social model of users' profiles that can be reusable in many application domains and for several social information filtering mechanisms, and proposing optimal techniques for enriching user's profile from the user's social network. We rely on existing studies in social sciences to propose a communities (rather than individuals) based approach for using individuals around the user in a specific part of his social network, to derive his social profile (profile that contains user's interest derived from his social network). The significant part of the user's social network used in our studies is composed of individuals located at a maximum distance k (in the entire social network) from the user, and relationships between these individuals (k-egocentric network). Two evaluations of the proposed approach based on communities in k-egocentric networks have been conducted in the online social network Facebook and the co-authors network DBLP. They allow us to demonstrate the relevance of the proposal with respect to existing individual based approaches, and the impact of structural measures such as the centrality of communities (degree or proximity) or user's k-egocentric network density, on the quality of results. Our approach opens up many opportunities for future studies in social information filtering and many application domains as well as on the Web (e.g. personalization of search engines, recommender systems in e-commerce, adaptive systems in e-Learning environment) or in Intranets business systems (e.g. behavioral analysis in networks of subscribers telecom customers, detection of abnormal behavior network bank customers, etc.)

The end of stigma? Understanding the dynamics of legitimisation in the context of TV series consumption

This research contributes to prior work on stigmatisation by looking at stigmatisation and legitimisation as social processes in the context of TV series consumption. Using in-depth interviews, we show that the dynamics of legitimisation are complex and accompanied by the reproduction of existing stigmas and creation of new stigmas

The everyday life and information practices of a natural immunity advocate

Parents continue to decline or delay immunizing their children resulting in outbreaks of vaccine-preventable disease across the United States. Efforts to profile these parents struggle to find demographic consensus or create a consistent profile of which parents make these problematic decisions. These parents are often assumed to be under the sway of misinformation, exacerbating the division between them and healthcare providers. Research about this population typically studies the behavior in isolate and in relation to the mainstream view where it is normative to vaccinate one’s children. This dissertation, a grounded theory embedded chronological case study of a vaccine-avoidant mommy blogger, describes a worldview where vaccination avoidance and delay is normative and documents related behaviors and beliefs that accompany not vaccinating one’s children. This was accomplished using multiple methods, particularly inductive coding, memoing, quantitative and qualitative content analysis, bibliometrics, and digital ethnography. Data streams included seven years of blog posts, videos, comment threads, information citations, artifacts endorsed or created by the blogger, and an assessment of family resemblances between the blogger and her digital peer network. From the data emerged a theoretical model that overlapped with Ludwik Fleck’s theory of thought collectives and Elfreda Chatman’s theory of information poverty. The findings include a set of family resemblances observed in the data and artifacts and assessed against a network of nearly 90 other cases. The findings include bibliometric evaluation of the information the blogger cited and discussion of her information behavior and how information operates within her worldview, where vaccine avoidance and delay is normative.Doctor of Philosoph