Thesis Defense - Ahmet Can Çimen (MSIE)

Ahmet Can Çimen - M.Sc. Industrial Engineering

Asst. Prof. Enis Kayış – Advisor

Date: 16.08.2021

Time: 10:00

Location: This meeting will be held ONLINE. Please send an e-mail to gizem.bakir@ozyegin.edu.tr in order to participate in this defense.

A Robust Longitudinal Model for Song Popularity: A Cross-Cultural Study

Thesis Committee:

Asst. Prof. Enis Kayış, Özyeğin University

Asst. Prof. Mehmet Yasin Ulukuş, Istanbul Technical University

Asst. Prof. Murat Kaya, Sabancı University

Abstract:

Usage of new generation music streaming platforms such as Spotify and Apple Music has increased rapidly in the last years. Understanding the music preferences of the user base is valuable for these firms, translating into higher customer satisfaction. In this study, we develop and compare several statistical models to quantify the effects of the different factors on music popularity by using acoustic and artist-related features. We compare results from three countries to understand whether there are any cultural differences in how much each of these factors affects song popularity. To compare the results, we use weekly top 200 charts and songs’ acoustic features as data sources. In addition to acoustic features, we add acoustic similarity, genre, song recentness features into the dataset. We apply the Flexible Least Squares (FLS) method and developed Optimal Stepwise Linear Regression (SLR) methods to and observe time-varying regression coefficients. We also propose a regression tree-based heuristic algorithm to solve the SLR problem in a reasonable time. The FLS method tries to keep the differences of adjacent weeks' coefficients as small as possible. On the other hand, the SLR does not control the variation between consecutive weeks' coefficients however, it only allows a limited number of coefficient changes over time. Coefficients that we obtain from the FLS method show that we can keep track of the changes of the factors that affect music listening habits and associate real-life events with these changes. Besides, with the heuristic method, we propose for the SLR method, we can quickly achieve near-optimal solutions which give us clues about the time of the significant changes in the music taste in different countries. Finally, inferences we made with the study may help the music industry grow and contribute to the anthropological fields for discovering socio-cultural/political aspects of the music taste changes.

Bio: 

Ahmet C. Çimen graduated from Mustafa Saffet Anatolian High School in 2013. He received his B.Sc. degree in Industrial Engineering from Özyeğin University in January 2018 and continued his education with joining the Master of Science program in Industrial Engineering at Özyeğin University, under the supervision of Assistant Professor Enis Kayış. His research mainly focuses on data science.