Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Locality sensitive hashing based scalable collaborative filtering, Yerele Duyarli Kiyim Tabanli Ölçeklenebilir Işbirlikçi Filtreleme(Institute of Electrical and Electronics Engineers Inc., 2015) Aytekin, Ahmet Maruf; Aytekin, Tevfik; Aytekin, Ahmet Maruf, Bilgisayar Mühendisliʇi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aytekin, Tevfik, Bilgisayar Mühendisliʇi Bölümü, Bahçeşehir Üniversitesi, Istanbul, TurkeyNeighborhood-based collaborative filtering methods are widely used in recommender systems because of their easy-to-implement and effective nature. One important drawback of these methods is that they do not scale well with increasing amounts of data. In this work we applied the locality sensitive hashing technique for solving the scalability problem of neighborhood-based collaborative filtering. We evaluate the effects of the parameters of locality sensitive hashing technique on the scalability and the accuracy of the developed recommender system. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Balanced random forest for imbalanced data streams, Dengesiz veri akimlari için dengelenmiş rassal orman(Institute of Electrical and Electronics Engineers Inc., 2016) Yağcı, A. Murat; Aytekin, Tevfik; Gürgen, Fïkret S.; Yağcı, A. Murat, Bilgisayar Mühendisliǧi Bölümü, Boğaziçi Üniversitesi, Bebek, Turkey; Aytekin, Tevfik, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gürgen, Fïkret S., Bilgisayar Mühendisliǧi Bölümü, Boğaziçi Üniversitesi, Bebek, TurkeyData with highly imbalanced class distributions are common in real life. Machine learning application domains such as e-commerce, risk management, environmental, and health monitoring often suffer from class imbalance since the interesting case occurs rarely. Yet another layer of complexity is added when data arrives as massive streams. In such a setting, it is often of interest that a learning algorithm is updated in an incremental fashion for scalability and model adaptivity reasons while still handling the class imbalance. In this paper, we propose an ensemble algorithm for imbalanced data streams based on the offline balanced random forest idea. We also show on a recent dataset that the algorithm is useful for the buyer prediction problem in large-scale recommender systems. © 2017 Elsevier B.V., All rights reserved.Publication Metadata only Short term water demand forecasting using regional data, Bölgesel veriler üzerinde yapilan kisa dönem su talep tahmini(Institute of Electrical and Electronics Engineers Inc., 2019) Zeynep Yildiz, Tugba; Aytekin, Tevfik; Zeynep Yildiz, Tugba,; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyLimited water resources and changing climatic conditions make water one of the critical natural resources. In order to manage this limited resource in the most effective way, real-time monitoring and automatic control systems are becoming increasingly popular. Water demand forecasting is one of the important subjects in these studies. Accurate water demand forecasting increases efficiency in the management of water networks and also allows for leak/fraud detection. In this work, we carry out short term water demand forecasting using water consumption data collected from water meters in a regional area. For forecasting, we first clean water consumption data, extract various features and apply machine learning methods for forecasting. After giving the experimental results we discuss future improvements. © 2020 Elsevier B.V., All rights reserved.