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Depuis ses fondements, le domaine de l’Interaction Homme-Machine (IHM) est marqué par le souci constant de concevoir et de produire des systèmes numériques utiles et utilisables, c’est-à-dire adaptés aux utilisateurs dans leur contexte. Vu le développement exponentiel des recherches dans les IHM, deux états des lieux s’imposent dans les environnements en ligne : le concept de confiance et le comportement de l’usager. Ces deux états ne cessent de proliférer dans la plupart des solutions conçues et sont à la croisée des travaux dans les interfaces de paiements en ligne et dans les systèmes de recommandation. Devant les progrès des solutions conçues, l’objectif de cette recherche réside dans le fait de mieux comprendre les différents enjeux dans ces deux domaines, apporter des améliorations et proposer de nouvelles solutions adéquates aux usagers en matière de perception et de comportement en ligne. Outre l’état de l’art et les problématiques, ce travail est divisé en cinq parties principales, chacune contribue à mieux enrichir l’expérience de l’usager en ligne en matière de paiement et recommandations en ligne : • Analyse des multi-craintes en ligne : nous analysons les différents facteurs des sites de commerce électronique qui influent directement sur le comportement des consommateurs en matière de prise de décision et de craintes en ligne. Nous élaborons une méthodologie pour mesurer avec précision le moment où surviennent la question de la confidentialité, les perceptions en ligne et les craintes de divulgation et de pertes financières. • Intégration de personnalisation, contrôle et paiement conditionnel : nous proposons une nouvelle plateforme de paiement en ligne qui supporte à la fois la personnalisation et les paiements multiples et conditionnels, tout en préservant la vie privée du détenteur de carte. • Exploration de l’interaction des usagers en ligne versus la sensibilisation à la cybersécurité : nous relatons une expérience de magasinage en ligne qui met en relief la perception du risque de cybercriminalité dans les activités en ligne et le comportement des utilisateurs lié à leur préoccupation en matière de confidentialité. • Équilibre entre utilité des données et vie privée : nous proposons un modèle de préservation de vie privée basé sur l’algorithme « k-means » et sur le modèle « k-coRating » afin de soutenir l’utilité des données dans les recommandations en ligne tout en préservant la vie privée des usagers. • Métrique de stabilité des préférences des utilisateurs : nous ciblons une meilleure méthode de recommandation qui respecte le changement des préférences des usagers par l’intermédiaire d’un réseau neural. Ce qui constitue une amélioration à la fois efficace et performante pour les systèmes de recommandation. Cette thèse porte essentiellement sur quatre aspects majeurs liés : 1) aux plateformes des paiements en ligne, 2) au comportement de l’usager dans les transactions de paiement en ligne (prise de décision, multi-craintes, cybersécurité, perception du risque), 3) à la stabilité de ses préférences dans les recommandations en ligne, 4) à l’équilibre entre vie privée et utilité des données en ligne pour les systèmes de recommandation. / Technologies in Human-Machine Interaction (HMI) are playing a vital role across the entire production process to design and deliver advanced digital systems. Given the exponential development of research in this field, two concepts are largely addressed to increase performance and efficiency of online environments: trust and user behavior. These two extents continue to proliferate in most designed solutions and are increasingly enriched by continuous investments in online payments and recommender systems. Along with the trend of digitalization, the objective of this research is to gain a better understanding of the various challenges in these two areas, make improvements and propose solutions more convenient to the users in terms of online perception and user behavior. In addition to the state of the art and challenges, this work is divided into five main parts, each one contributes to better enrich the online user experience in both online payments and system recommendations: • Online customer fears: We analyze different components of the website that may affect customer behavior in decision-making and online fears. We focus on customer perceptions regarding privacy violations and financial loss. We examine the influence on trust and payment security perception as well as their joint effect on three fundamentally important customers’ aspects: confidentiality, privacy concerns and financial fear perception. • Personalization, control and conditional payment: we propose a new online payment platform that supports both personalization and conditional multi-payments, while preserving the privacy of the cardholder. • Exploring user behavior and cybersecurity knowledge: we design a new website to conduct an experimental study in online shopping. The results highlight the impact of user’s perception in cybersecurity and privacy concerns on his online behavior when dealing with shopping activities. • Balance between data utility and user privacy: we propose a privacy-preserving method based on the “k-means” algorithm and the “k-coRating” model to support the utility of data in online recommendations while preserving user’s privacy. • User interest constancy metric: we propose a neural network to predict the user’s interests in recommender systems. Our aim is to provide an efficient method that respects the constancy and variations in user preferences. In this thesis, we focus on four major contributions related to: 1) online payment platforms, 2) user behavior in online payments regarding decision making, multi-fears and cyber security 3) user interest constancy in online recommendations, 4) balance between privacy and utility of online data in recommender systems.

M. Tech. (Department of Information Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Too many options on the internet are the reason for the information overload problem to obtain relevant information. A recommender system is a technique that filters information from large sets of data and recommends the most relevant ones based on people‟s preferences. Collaborative and content-based techniques are the core techniques used to implement a recommender system. A combined use of both collaborative and content-based techniques called hybrid techniques provide relatively good recommendations by avoiding common problems arising from each technique. In this research, a proactive University Library Book Recommender System has been proposed in which hybrid filtering is used for enhanced and more accurate recommendations. The prototype designed was able to recommend the highest ten books for each user. We evaluated the accuracy of the results using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). A measure value of 0.84904 MAE and 0.9579 RMSE found by our system shows that the combined use of both techniques gives an improved prediction accuracy for the University Library Book Recommender System.

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Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.

Despite the many technological advancements made in the music industry in recent years, there is still not a single widely adopted platform for musicians to play music together online. In 2020, the Covid-19 pandemic and the subsequent quarantine pushed the need for such a platform into the spotlight. As a response, the music company Elk Audio launched their new product: Aloha. Aloha is a combined hardware and web application that allows musicians to play music in real-time over the Internet. Aloha is currently only intended for musicians who already know each other to connect and play. However, Elk's ambition is to make it the go-to platform for musicians to expand their network.  The purpose of this Master's Thesis is to design the next version of the web application of Aloha, focusing on social interactions. This Master's Thesis investigates musicians' current social and musical behaviour, and their opinions of how this can be done online. Qualitative data was collected by performing semi-structured interviews with musicians of different backgrounds. The study revealed many different goals and needs of potential users of Aloha. Furthermore, we found several determining factors which enable and encourage musicians to form new musical relationships online. The final suggested design is based on the analysed data and founded in theory regarding persuasive and recommending system design, among others. / Trots de tekniska framstegen som gjorts inom musikindustrin de senaste åren, finns det fortfarande inte ett enda allmänt accepterat alternativ för musiker att spela musik tillsammans online. Covid-19 pandemin och den åtföljande karantänten förde behovet för en sådan plattform till rampljuset. I början av år 2020 insåg musikföretaget Elk Audio att de kunde fylla denna lucka med sin nya produkt: Aloha. Aloha är en kombinerad hårdvara och webbapplikation som möjliggör musiker att spela musik i realtid över Internet. Aloha är för närvarande endast avsedd för musiker som redan känner varandra. Elks ambition är dock att göra Aloha till en plattform för musiker där de kan utöka sitt musikaliska nätverk. Syftet med detta examensarbete är att utforma nästa version av Alohas webbapplikation, med fokus på sociala interaktioner. Detta examensarbete undersöker musikers nuvarande sociala och musikaliska beteenden och deras åsikter om musikaliska interaktioner online. Kvalitativa data samlades in genom att utföra halvstrukturerade intervjuer med musiker från olika bakgrunder. Studien avslöjade de många olika målen och behoven hos potentiella användare av Aloha. Dessutom fann vi flera avgörande faktorer som möjliggör och uppmuntrar musiker att skapa och underhålla nya musikrelationer online. Den slutliga föreslagna designen baseras på det analyserade datat och grundas i teori om design av rekommendationssystem, m. fl..