Kampanya katılım tahminleme için derin öğrenme modelleri geliştirilmesi

Companies engage with their customers in order to establish a long-term relationship. Targeting the right audience with the right product is crucial for providing better services to customers, increasing their loyalty to the company, and gaining high profit. Therefore, companies make huge investments to build campaign management systems, which are mostly rule-based and highly depend on business insight and human expertise. In the last decade, recommendation systems usually use modeling techniques such as deep learning to understand and predict the interests of customers. Classic deep neural networks are good at learning hidden relations within data (generalization); however, they have limited capability for memorization. Wide & Deep network model, which is originally proposed for Google Play App. recommendation deals with this problem by combining Wide and Deep network models in a joint network. However, this model requires domain expert knowledge and manually crafted features to benefit from memorization. In this thesis, we advocate using Wide & Deep network models for campaign participation prediction, particularly in the area of telecommunication. To deal with the aforementioned issue with that model, this thesis introduces the idea of using decision trees for automatic creation of combinatorial features (cross-product transformations of existing features) instead of demanding them from human experts. A set of comprehensive experiments on campaign participation data from a leading GSM provider has been conducted. The results have shown that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features. Furthermore, since a limited number of access to the customers is allowed, making well-targeted offers that are likely to be acceptable by the customers plays a crucial role. Therefore, an effective campaign participation prediction require to avoid false-positive predictions. Accordingly, we extended our research towards classification uncertainty to build network models that can predict whether or not they will fail. Consequently, we adopt evidential deep learning models to capture the uncertainty in prediction. Our experimental evaluation regarding prediction uncertainty has shown that the proposed approach is more confident for correct predictions while it is more uncertain for inaccurate predictions.