Recommendation systems have been widely utilized in e-commerce settings to aid users through their shopping experiences. The principal advantage of these systems is their ability to narrow down the purchase options in addition to marketing items to customers. However, a number of challenges remain, notably those related to obtaining a clearer understanding of users, their profiles, and their preferences in terms of purchased items. Specifically, recommender systems based on collaborative filtering recommend items that have been rated by other users with preferences similar to those of the targeted users. Intuitively, the more information and ratings collected about the user, the more accurate are the recommendations such systems suggest. In a typical recommender systems database, the data are sparse. Sparsity occurs when the number of ratings obtained by the users is much lower than the number required to build a prediction model. This usually occurs because of the users’ reluctance to share their reviews, either due to privacy issues or an unwillingness to make the extra effort. Grey-sheep users pose another challenge. These are users who shared their reviews and ratings yet disagree with the majority in the systems. The current state-of-the-art typically treats these users as outliers and removes them from the system. Our goal is to determine whether keeping these users in the system may benefit learning. Thirdly, cold-start problems refer to the scenario whereby a new item or user enters the system and is another area of active research. In this case, the system will have no information about the new user or item, making it problematic to find a correlation with others in the system. This thesis addresses the three above-mentioned research challenges through the development of machine learning methods for use within the recommendation system setting. First, we focus on the label and data sparsity though the development of the Hybrid Cluster analysis and Classification learning (HCC-Learn) framework, combining supervised and unsupervised learning methods. We show that combining classification algorithms such as k-nearest neighbors and ensembles based on feature subspaces with cluster analysis algorithms such as expectation maximization, hierarchical clustering, canopy, k-means, and cascade k-means methods, generally produces high-quality results when applied to benchmark datasets. That is, cluster analysis clearly benefits the learning process, leading to high predictive accuracies for existing users. Second, to address the cold-start problem, we present the Popular Users Personalized Predictions (PUPP-DA) framework. This framework combines cluster analysis and active learning, or so-called user-in-the-loop, to assign new customers to the most appropriate groups in our framework. Based on our findings from the HCC-Learn framework, we employ the expectation maximization soft clustering technique to create our user segmentations in the PUPP-DA framework, and we further incorporate Convolutional Neural Networks into our design. Our results show the benefits of user segmentation based on soft clustering and the use of active learning to improve predictions for new users. Furthermore, our findings show that focusing on frequent or popular users clearly improves classification accuracy. In addition, we demonstrate that deep learning outperforms machine learning techniques, notably resulting in more accurate predictions for individual users. Thirdly, we address the grey-sheep problem in our Grey-sheep One-class Recommendations (GSOR) framework. The existence of grey-sheep users in the system results in a class imbalance whereby the majority of users will belong to one class and a small portion (grey-sheep users) will fall into the minority class. In this framework, we use one-class classification to provide a class structure for the training examples. As a pre-assessment stage, we assess the characteristics of grey-sheep users and study their impact on model accuracy. Next, as mentioned above, we utilize one-class learning, whereby we focus on the majority class to first learn the decision boundary in order to generate prediction lists for the grey-sheep (minority class). Our results indicate that including grey-sheep users in the training step, as opposed to treating them as outliers and removing them prior to learning, has a positive impact on the general predictive accuracy.

Un système de recommandation a pour objectif de recommander à un utilisateur, appelé utilisateur actif, des ressources pertinentes pour lui. Le filtrage collaboratif (FC) est une approche de recommandation très répandue qui exploite les préférences exprimées par des utilisateurs sur des ressources. Le FC repose sur l'hypothèse que les préférences des utilisateurs sont cohérentes entre elles, ce qui permet d'inférer les préférences d'un utilisateur à partir des préférences des autres utilisateurs. Définissons une préférence spécifique comme une préférence qui ne serait partagée pour aucun groupe d'utilisateurs. Un utilisateur possédant plusieurs préférences spécifiques qu'il ne partage avec aucun autre utilisateur sera probablement mal servi par une approche de FC classique. Il s'agit du problème des Grey Sheep Users (GSU). Dans cette thèse, je réponds à trois questions distinctes. 1) Qu'est-ce qu'une préférence spécifique ? J'apporte une réponse en proposant des hypothèses associées que je valide expérimentalement. 2) Comment identifier les GSU dans les données ? Cette identification est importante afin d'anticiper les mauvaises recommandations qui seront fournies à ces utilisateurs. Je propose des mesures numériques permettant d'identifier les GSU dans un jeu de données de recommandation sociale. Ces mesures sont significativement plus performantes que celles de l'état de l'art. Enfin, comment modéliser ces GSU pour améliorer la qualité des recommandations qui leurs sont fournies ? Je propose des méthodes inspirées du domaine de l'apprentissage automatique et dédiées à la modélisation des GSU permettant d'améliorer la qualité des recommandations qui leurs sont fournies / A recommender system aims at providing relevant resources to a user, named the active user. To allow this recommendation, the system exploits the information it has collected about the active user or about resources. The collaborative filtering (CF) is a widely used recommandation approach. The data exploited by CF are the preferences expressed by users on resources. CF is based on the assumption that preferences are consistent between users, allowing a user's preferences to be inferred from the preferences of other users. In a CF-based recommender system, at least one user community has to share the preferences of the active user to provide him with high quality recommendations. Let us define a specific preference as a preference that is not shared by any group of user. A user with several specific preferences will likely be poorly served by a classic CF approach. This is the problem of Grey Sheep Users (GSU). In this thesis, I focus on three separate questions. 1) What is a specific preference? I give an answer by proposing associated hypotheses that I validate experimentally. 2) How to identify GSU in preference data? This identification is important to anticipate the low quality recommendations that will be provided to these users. I propose numerical indicators to identify GSU in a social recommendation dataset. These indicators outperform those of the state of the art and allow to isolate users whose quality of recommendations is very low. 3) How can I model GSU to improve the quality of the recommendations they receive? I propose new recommendation approaches to allow GSU to benefit from the opinions of other users