Sosyal Bilimler Enstitüsü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12573/218

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Analysis of online marketplace sales prediction based on machine learning algorithms: A case of Turkish e-commerce site
(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2023) Kaya, Ecem; AGÜ, Sosyal Bilimler Enstitüsü, İşletme ve Ekonomi İçin Veri Bilimi Ana Bilim Dalı
Internet shopping has grown in popularity as more of our daily requirements have begun to be addressed online. Learning about the preferences and motivations of customers in the Turkish market and guiding e-commerce platforms to adapt their marketing strategies and increase customer satisfaction is important for both resource allocation and cost minimization. The purpose of this paper is to estimate future sales for popular e-commerce sites based on behavioral factors such as discounts, price or free shipping. Therefore, real-time and experiment-independent data are collected from the sales made by one of Turkey's most popular e-commerce sites. In order to produce predictions, we employ Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbors Regressor, OLS regression, and Nu-Support Vector Regressor. The models developed using machine learning algorithms attempt to estimate the number of sales based on independent factors such as price, discount rate, and user ratings. As the result of this research, we calculate and compare the accuracy of the models with root mean squared errors and R².
Movie Recommendation Systems Based on Collaborative Filtering: A Case Study on Netflix
(Erciyes Üniversitesi, 2021) Sütçü, Muhammed; Erdem, Oğuzkan; Kaya, Ecem; 0000-0002-4634-7638; 0000-0002-8547-7929; 0000-0002-8523-9103; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Erdem, Oğuzkan
User ratings on items like movies, songs, and shopping products are used by Recommendation Systems (RS) to predict user preferences for items that have not been rated. RS has been utilized to give suggestions to users in various domains and one of the applications of RS is movie recommendation. In this domain, three general algorithms are applied; Collaborative Filtering that provides prediction based on similarities among users, Content-Based Filtering that is fed from the relation between item-user pairs and Hybrid Filtering one which combines these two algorithms. In this paper, we discuss which methods are more efficient in movie recommendation in the framework of Collaborative Filtering. In our analysis, we use Netflix Prize dataset and compare well-known Collaborative Filtering methods which are Singular Value Decomposition, Singular Value Decomposition++, KNearest Neighbour and Co-Clustering. The error of each method is calculated by using Root Mean Square Error (RMSE). Finally, we conclude that K-Nearest Neighbour method is more successful in our dataset.

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