Improved personalized suggestions on websites using machine learning

Abstract: Automated web personalization is a desired feature both for a website visitor and owner. The visitor is released from the burden of selecting settings or use search tools to adapt the website for their needs and find the right content. The owner benefits from having the visitors find content on the website they otherwise would have overlooked. In this project an existing model for the program Red Pine was improved on. The program Red Pine is a solution which can provide web personalization to website visitors by suggesting content based on the available information on the visitor. To predict which offer a visitor should be suggested the program uses a model with a classification algorithm. The model is trained with previous instances of made suggestions and the recorded responses from the visitors. The model was improved through selecting features, evaluating algorithm performance, and tuning algorithm parameters. Eight different algorithms from Azure Machine Learning Studio were used for the experiments. This resulted in finding an improved combination of features, algorithm parameters and algorithm for the model. New features describing the current session were added to the model and a correlation based filtering method for feature selection was used to discover relevant features. The identified features from the filtering was used in a greedy search to find an improved feature set. The default parameter values of the algorithm were tuned and the final combination of features, algorithm parameters and algorithm was cross evaluated. It is concluded that session data can be used as a replacement or complement for visitor data, when visitor data is unavailable, to get personalization with equal performance. Two of the algorithms that obtained highest performance scores had a linear decision boundary.