Target Marketing in Digital Gambling

Adaptive web systems are designed to predict user behaviors during live interaction. They prevent overload of information and personalize browsing experience. This thesis examines a front-end method to implement tracking and recommendation systems, which are scalable and can serve different use cases. The objectives are to build a prototype system based on the suggested method and analyze its performance. This thesis also discusses a method of combining different analysis to improve the final results relevancy. The abundance of available web services and user data to web services in the last decade poses new challenges to study user behaviors. While several techniques are developed to optimize data mining results, few have discussed about the actual implementation. Based on previous work in the field, a framework for development of recommendation system is presented. This thesis evaluates a front-end technique to implement a data collection and web personalization system that utilizes computational outcome from a data mining algorithm. The result of this thesis can serve as a guideline for implementation of a similar system in the future as well as further fine-tune analytical results