Mobil uygulama tavsiyelerinde kullanıcı profilleri ve kullanım tercihlerinin analizi.

In this thesis, we investigated the relationship between personality features and mobile technology use, particularly the use of traditional communication channels such as voice calls and SMS messaging and rapidly evolving mobile applications, specifically in communication and commerce domains. This was the first study to investigate the relationship between different personality features and different aspects of mobile technology use to this extent. The rapid growth in the mobile application market presents a significant challenge for users in terms of finding interesting and relevant applications. Recommendation systems deal with ends that are consumed by users in which similarity between consumer tastes is generally taken into account. In almost all of the application stores mobile applications are grouped under headings that employ consensus or authority influence strategies such as the most popular, most downloaded, editor’s choice or applications of the day. However, the literature contains limited information about the users’ perception of such influence strategies and the underlying factors that lie beyond the users’ preferences. In this thesis, influence strategies used in mobile application recommendations and the users’ compliance to the recommendations are investigated in an experimental context. Two complementary experiments are conducted; one on the web and the other on a mobile platform. The users’ compliance with mobile application recommendations and the question of how the personality features of users affect their compliance with recommendations are explored in the experiments. Furthermore, this thesis analyses the applications owned by the user, the composition of applications under categories and their relation with personality features. The main contribution of this thesis is to ground the design of recommending systems in an understanding of users’ needs and to provide means for personalization to better address users’ individual differences.Ph.D. - Doctoral Progra

A workflow for synthetic data generation and predictive maintenance for vibration data

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.publishedVersio

What installed mobile applications tell about their owners and how they affect users' download behavior

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. An experimental study was conducted through the use of a specifically designed mobile application, on users' mobile phones. The goals were; first, to learn about the users' personality and the applications they downloaded to their mobile phones, second to recommend applications to users via notifications through the use of experimental mobile application and learn about user behavior in mobile environment. The question of how the personality features of users affect their compliance to recommendations is explored in this study. It is found that conscientiousness is positively related with accepting recommended applications and being agreeable is related with the preference for the applications of editor's choice. Furthermore, in this study, applications owned by the user and the composition of applications under categories and their relation with personality features are explored. It is shown that the number of user owned applications and their category differ according to gender and personality. Having similar applications and the number of applications owned under specific categories increase the probability of accepting recommended applications

An Exploratory Study on the Outcomes of Influence Strategiesin Mobile Application Recommendations

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. Recommendation systems deal with ends such as movies and consumer goods that are consumed by users where similarity between consumer tastes is generally taken into account. On the other hand, recommendation systems for mobile applications differ from traditional systems in terms of the characteristics of the ends they recommend. They present applications that are not just the ends for consumption but also means to reach various ends. In almost all application stores mobile applications are grouped under headings that employ consensus or authority influence strategies such as the most popular, most downloaded, editor's choice or applications of the day. However in the literature, there is limited information about the users' perception of such influence strategies and underlying factors that lie beyond the users' preferences. The traditional persuasion literature suggests that people are more likely to accept recommendations when the sources display persuasive messages during the interaction. However the effect of visibility modality in the display has not been extensively studied. The effects of visible and semi-visible persuasive messages are analyzed and compared in this study. The users' compliance with persuasive messages in the mobile application recommendation domain is examined. The question of how the persuadability of users affects their compliance is further explored

Impact of individual differences on the use of mobile phones and applications

Many studies examining the relation between mobile phone use and the personality traits of individual users have concluded that a significant relationship exists with extraversion standing out as a common trait in this context. In addition, innovativeness plays a key role in the users' adoption of technology and this has been studied within the domain of information systems including the adoption of mobile commerce. This study investigates the relationship between innovativeness, extraversion, mobile phone use and mobile applications, for the first time in the literature. A structured survey was administered to 343 university students. The results showed that mobile phone use features are predominantly related with the trait of extraversion whereas mobile application use features are mostly related with the innovativeness of the user

A Study on User Perception of Mobile Commerce for Android and iOS Device Users

ustomer profiling in the mobile commerce (m-commerce) domain has recently gained importance due to the increased proliferation of smartphones and tablets. One of the major challenges confronting m-commerce developers is the need to know user perceptions of m-commerce applications in order to better design and deliver m-commerce services. In this paper, user perceptions of mobile commerce applications is analyzed based on their gender and the operating system (OS) of the devices in use, which are important factors for user profiling in mobile business models. The results show that there is a significant difference between Android and iOS device users' perceptions of mobile commerce applications except for their perception of advertisements. On the other hand, user perceptions of mobile applications in general does not exhibit significant differences except for the perception of usefulness for both gender and OS

A workflow for synthetic data generation and predictive maintenance for vibration data

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area

Çayın Türkiye'ye gelişi, ilk çay fabrikasının kuruluşu ve çay üretimini artırmak için yapılan çalışmalar

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2012.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Erdem Sönmez.Sönmez, Erdem. HIST 200-14SÖNMEZ HIST 200-14/J 2011-1