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    Benchmarking of Recommendation Models for an On-Line Fast Fashion Retailer
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tilkat, Mustafa; Küçükaydın, Hande
    This project studies the usage of the recommendation engines to improve the sales in an online fashion retailer. Fashion retailers sale variety of products throughout their online channels. Since the number of products can be huge compared to an in-line shop, customers may miss some of them while shopping online. Hence, it is crucial to display products that are more likely to be purchased by a customer when the customer is surfing on the website. Our problem is motivated by practice at an online fashion retailer in Turkey. Four collaborative filtering-based algorithms and a random recommender are utilized to design a recommendation engine. 80% of the data is used for training while the other 20% is to used test the designed method. Based on our experiments, User Based Collaborative Filtering (UBCF) using Pearson correlation outperform the other algorithms based on Receiver Operating Characteristic (ROC) curve.