Title: MOBILE USER DATA MINING FOR INFERRING INDIVIDUAL’S DIFFERENCES IN INFLUENCE STRATEGIES
PhD Candidate: Şeyma Çavdar
Program: Informations Systems Department
Date: 28 August 13:00
Place: Conference Hall-01
Abstract: Owing to the widespread and ubiquitous nature of mobile technologies, a large amount of data about users including location, access and interaction behavior is currently available. These data have recently become important as it has the potential to reveal personal information and user characteristics such as personality traits. In the literature, data of mobile phone use (such as number of calls, messages) is generally collected with questionnaires or special applications and then analyzed. However, self-reported data is often difficult to collect as well as mobile call data is limited for inferring user preferences and characteristics. In addition, people increasingly use different types of mobile applications. In this thesis, personal data obtained by mobile applications will be used and analyzed with data mining techniques in order to infer personality characteristics of users (e.g. BIG5) and how they are affected by influence strategies (e.g. Cialdini’s six strategies). The results are expected to improve personalization of mobile applications and develop successful user profiles. The health domain will be selected in this thesis.