Free-to-play gamers' spending habits are crucial for game developers to generate revenue in games. The unique thing about free-to-play business model games is that players don't have to pay to play the game, but need to make in-game purchases if they want a better gaming experience. The users are diverse，the trend to personalise services for users based on their diversity has become unavoidable, but identifying user needs is a large and complex problem, so an in-depth understanding of players' spending behaviour in games is essential. This thesis assists researchers in addressing this issue by constructing a game user profile based on player consumption patterns. In order to build a profile of a game's users, a great amount of data needs to be collected. However, simply providing raw data may not be of value to the development. For this reason, this thesis explores a methodology for effectively analysing and communicating survey data. This methodology collects data from user surveys, analyses the data using the author's proposed logic for creating user profiles based on consumer behaviour, and uses a combination of quantitative and qualitative methods to create a realistic game user profile. On this basis, through the application of the proposed logic, four categories of player consumption archetypes could be identified: players with Positive attitude(POSA), Moderate attitude (MODA), Reluctance attitude (RELA), and Negative attitude (NEGA). Subsequently, these archetypes could then be used to establish design guidelines for creating game mechanics and services to better encourage in-game purchases. However, as these methods have not been put into practice, the effectiveness of using them is open to question.

Up to now various recommendation systems have been proposed for web based applications such as e-commerce and information retrieval where a large amount of product or information is available. Basically, the task of the recommendation systems in those applications, for example the e-commerce, is to find and recommend the most relevant items to users/customers. In this domain, the most prominent approaches are collaborative filtering and content-based filtering. Sometimes these approaches are called as user profiling as well. In this work, a context-aware recommendation system is proposed for mobile environment, which also can be considered as an extension of those recommendation systems proposed for web-based information retrieval and e-commerce applications. In the web-based information retrieval and e-commerce applications, for example in an online book store (e-commerce), the users&amp / #8217 / actions are independent of their instant context (location, time&amp / #8230 / etc). But as for mobile environment, the users&amp / #8217 / actions are strictly dependent on their instant context. These dependencies give raise to need of filtering items/actions with respect to the users&amp / #8217 / instant context. In this thesis, an approach coupling approaches from two different domains, one is the mobile environment and other is the web, is proposed. Hence, it will be possible to separate whole approach into two phases: context-aware prediction and user profiling. In the first phase, combination of two methods called fuzzy c-means clustering and learning automata will be used to predict the mobile user&amp / #8217 / s motions in context space beforehand. This provides elimination of a large amount of items placed in the context space. In the second phase, hierarchical fuzzy clustering for users profiling will be used to determine the best recommendation among the remaining items.

På uppdrag av sportmediakoncernen ESMG undersöker detta arbete två frågeställningar: Hur kan man utveckla och driftsätta ett rekommendationssystem för nyhetsartiklar? Vilka föroch nackdelar finns med ett eget system jämfört med tredjepartssystem? Arbetet använder Polyas fyra steg som undersökningsmetod, där de fyra stegen anpassas och appliceras på detta projekt. För att kunna besvara den första frågeställningen, skapas initialt en kravspecifikation, som ligger till grund för rekommendationssystemets funktionella och icke-funktionella krav. Utifrån kravspecifikationen, görs en initial fallstudie av Amazon Web Services (AWS), där lämpliga verktyg och tjänster väljs, följt av utformning av en arkitektur för rekommendationssystemet. I en fallstudie av Hockeysveriges webbplats, implementeras sedan arkitekturen med hjälp av AWS och några andra verktyg, som Google Tag Manager och Numeri. Slutligen utvärderas arbetet för kravuppfyllnad. För att kunna besvara den andra frågeställningen, görs en summativ utvärdering av ett antal olika tredjepartssystem för rekommendationer. Genom analys av tredjepartssystemens respektive webbplatser, tas listor på föroch nackdelar fram, ackompanjerat med korta beskrivningar av tjänsterna. Resultaten av den första frågeställningen är en lösning, som visar hur man i praktiken kan utveckla och driftsätta ett rekommendationssystem för nyhetsartiklar. Genom en detaljerad beskrivning alla delar av utvecklingen, fungerar resultaten som en konkret guide i skapande av rekommendationssystem med moderna verktyg. Med avseende på arbetets andra frågeställning, visar resultaten att den stora skillnaden mellan ett egenbyggt system och tredjepartssystem ligger i flexibiliteten, men att ett eget system kommer med mer ansvar, fler beroenden och utan annan funktionalitet som statistik, vilket ofta ingår i tredjepartssystem. / On behalf of the corporate group ESMG, this thesis examines two research questions: How can one develop and deploy a custom recommender system for news articles? What are the pros and cons of having a custom system, compared to third-party systems? The thesis utilizes Polya's four steps as its research method, where the four steps are adapted and applied to this particular project. In order to answer the first research question, an initial requirements specification is created, which provides the basis for the recommender system's functional and non-functional requirements. Based on the requirement specification, an initial case study of Amazon Web Services (AWS) is performed, where appropriate tools and services are selected, followed by the design of an architecture for the recommender system. In a case study of ESMG:s website Hockeysverige, the architecture is then implemented, using AWS and some other necessary tools, such as Google Tag Manager and Numeri. Finally, the implementation is evaluated with respect to requirement compliance. To answer the second research question, a summative evaluation of a number of different third-party recommender systems is performed. By analyzing the third-party systems' websites, a list of pros and cons is presented, accompanied by a brief description of the service. The results of the first research question, is a solution which illustrates how one can, in practice, implement a news recommender system. Through a detailed description of all aspects of development, the results function as a guide in creating recommendation systems using modern tools. With regard to the second research question, the results show that the major difference between a custom system and third-party systems, lies in the flexibility, but a custom system brings more responsibility, more dependencies, and no other functionality, such as statistics, which is often part of third-party systems.

The popularity of e-commerce sites and applications that use recommendations and user modeling is increased recently. The development and contest in tourism calls attention of large-scale IT companies. These companies have started to work on recommendation systems and user modeling on tourism sector. Some of the clustering methodologies, neighboring methods and machine learning algorithms are commenced to use for making predictions about tourist&rsquo / s interests while he/she is traveling around the city. Recommendation ability is the most interesting thing for a tourist guide application. Recommender systems are composed of two main approaches, collaborative and content-based filtering. Collaborative filtering algorithms look for people that have similar interests and properties, while contentbased filtering methods pay attention to sole user&rsquo / s interests and properties to make recommendations. Both of the approaches have advantages and disadvantages, for that reason sometimes these two approaches are used together. Chosen method directly affects the recommendation quality, so advantages and disadvantages of both methods will be examined carefully. Recommendation of locations or services can be seen as a classification problem. Artificial intelligent systems like neural networks, genetic algorithms, particle swarm optimization algorithms, artificial immune systems are inspired from natural life and can be used as classifier systems. Artificial immune system, inspired from human immune system, has ability to classify huge numbers of different patterns. In this paper ESGuide, a tourist guide application that uses artificial immune system is examined. ESGuide application is a client-server application that helps tourists while they are traveling around the city. ESGuide has two components: Map agent and recommender agent. Map agent helps the tourist while he/she interacts with the city map. Tourist should rate the locations and items while traveling. Due to these ratings and client-server interaction, recommender agent tries to predict user interested places and items. Tourist has a chance to state if he/she likes the recommendation or not. If the tourist does not like the recommendation, new recommendation set is created and presented to the user.

L'incroyable développement du World Wide Web a permis la création de nouveaux métiers, services, ainsi que de nouveaux moyens de partage de connaissance. Le web attire aussi des malfaiteurs, qui le considèrent comme un moyen pour gagner de l'argent en exploitant les services et la propriété d'autrui. Cette thèse propose une étude des sites web compromis et malicieux sous plusieurs axes d'analyse. Même si les attaques web peuvent être de nature très compliquées, on peut quasiment toujours identifier quatre acteurs principaux dans chaque cas. Ceux sont les attaquants, les sites vulnérables hébergés par des fournisseurs d'hébergement, les utilisateurs (souvent victimes des attaques), et les sociétés de sécurité qui parcourent Internet à la recherche de sites web compromis à être bloqués. Dans cette thèse, nous analysons premièrement les attaques web du point de vue des hébergeurs, en montrant que, même si des outils gratuits permettent de détecter des signes simples de compromission, la majorité des hébergeurs échouent dans cette épreuve. Nous passons en suite à l'analyse des attaquants et des leurs motivations, en étudiant les attaques web collectés par des centaines de sites web vulnérables. Ensuite, nous étudions le comportement de milliers de victimes d'attaques web, en analysant leurs habitudes pendant la navigation, pour estimer s'il est possible de créer des "profils de risque", de façon similaire à ce que les compagnies d'assurance font aujourd'hui. Enfin, nous adoptons le point de vue des sociétés de sécurité, en proposant une solution efficace pour la détection d'attaques web convoyées par sites web compromis / The incredible growth of the World Wide Web has allowed society to create new jobs, marketplaces, as well as new ways of sharing information and money. Unfortunately, however, the web also attracts miscreants who see it as a means of making money by abusing services and other people's property. In this dissertation, we perform a multidimensional analysis of attacks involving malicious or compromised websites, by observing that, while web attacks can be very complex in nature, they generally involve four main actors. These are the attackers, the vulnerable websites hosted on the premises of hosting providers, the web users who end up being victims of attacks, and the security companies who scan the Internet trying to block malicious or compromised websites. In particular, we first analyze web attacks from a hosting provider's point of view, showing that, while simple and free security measures should allow to detect simple signs of compromise on customers' websites, most hosting providers fail to do so. Second, we switch our point of view on the attackers, by studying their modus operandi and their goals in a distributed experiment involving the collection of attacks performed against hundreds of vulnerable web sites. Third, we observe the behavior of victims of web attacks, based on the analysis of their browsing habits. This allows us to understand if it would be feasible to build risk profiles for web users, similarly to what insurance companies do. Finally, we adopt the point of view of security companies and focus on finding an efficient solution to detecting web attacks that spread on compromised websites, and infect thousands of web users every day