Optimization and personalization of a web service based on temporal information

Development in information and communication technology has increased the attention of personalization in the 21st century and the benefits to both marketers and customers are claimed to be many. The need to efficiently deliver personalized content in different web applications has increased the interest in the field of machine learning. In this thesis project, the aim is to develop a decision model that autonomously optimizes a commercial web service to increase the click through rate. The model should be based on previously collected data about previous usage of the web service. Different requirements for efficiency and storage must be fulfilled at the same time as the model should produce valuable results. An algorithm for a binary decision tree is presented in this report. The evolution of the binary tree is controlled by an entropy minimizing heuristic approach together with three specified stopping criteria. Tests on both synthetic and real data sets were performed to evaluate the accuracy and efficiency of the algorithm. The results showed that the running time is dominated by different parameters depending on the sizes of the test sets. The model is capable of capturing inherent patterns in the the available data