Browsing by Author "Ayvaz, Demet"

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Campaign participation prediction with deep learning
(Elsevier, 2021-08) Ayvaz, Demet; Aydoğan, Reyhan; Akçura, Munir Tolga; Şensoy, Murat; Business Administration; Computer Science; AYDOĞAN, Reyhan; AKÇURA, Münir Tolga; ŞENSOY, Murat; Ayvaz, Demet
Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to offer, creating intelligent campaign management systems that can automate such decisions is essential. In addition, regulations or company policies usually restrict the number of accesses to the customers. Efficient learning of customer behaviour through dynamic campaign participation observations becomes a crucial feature that may ultimately define customer satisfaction and retention. This paper builds on the recent successes of deep learning techniques and proposes a classification model to predict customer responses for campaigns. Classic deep neural networks are good at learning hidden relations within data (i.e., patterns) but with limited capability for memorization. One solution to increase memorization is to use manually craft features, as in Wide & Deep networks, which are originally proposed for Google Play App. recommendations. We advocate using decision trees as an easier way of mining high-level relationships for enhancing Wide & Deep networks. Such an approach has the added benefit of beating manually created rules, which, most of the time, use incomplete data and have biases. A set of comprehensive experiments on campaign participation data from a leading GSM provider shows that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features.
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Enhancing deep learning models for campaign participation prediction
(2019-07-31) Ayvaz, Demet; Şensoy, Murat; Şensoy, Murat; Kıraç, Furkan; Akçura, Munir Tolga; Tek, B.; Alkaya, A. F.; Department of Computer Science; Ayvaz, Demet
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 pro t. 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 & Deep network models in a joint network. However, this model requires domain expert knowledge and manually crafted features to bene t 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 signi cant 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 o ers that are likely to be acceptable by the customers plays a crucial role. Therefore, an e ective campaign participation prediction require to avoid falsepositive predictions. Accordingly, we extended our research towards classi cation 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 con dent for correct predictions while it is more uncertain for inaccurate predictions.
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On characterizing sectoral interactions via connections between employees in professional online social networks
(Elsevier, 2018-12) Ayvaz, Demet; Gürsun, Gonca; Özlale, Ümit; Economics; Computer Science; GÜRSUN, Gonca; ÖZLALE, Ümit; Ayvaz, Demet
The collaboration among individuals is essential to maximize economic efficiency. Today most of the technological and economical advancements require multidisciplinary efforts. Therefore promoting interaction and knowledge sharing between industry sectors within a country is more crucial than ever. One main platform for such communication is business-oriented online social networks where thousands of professionals from various sectors connect with each other. These social networks provide a way of disseminating the latest information in technology and business. Our goal in this paper is to analyze the connectivity patterns of individuals in a business-oriented social network as a tool to understand how industry sectors are represented and interact with each other in such online platforms. To do that, we collect profiles of thousands of employees from a professional online social network. Then, first, we analyze the structural properties of the network and report its characteristics in comparison with the non-professional ones. Second, we map each employee to the sector she works in and study the connectivity patterns within each sector separately. We find that the connectivity patterns within sectors vary and the employees within a sector do not necessarily form densely connected communities. Third, we investigate the relationship between sectors via the connectivity of their employees and identify the main social clusters of sectors. We show that there are significant similarities between social connectivity and the economic transactions between sectors.