Browsing by Author "Kurt,Z."
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Article Citation Count: 1A Graph-Based Recommendation Algorithm on Quaternion Algebra(Springer, 2022) Kurt, Zühal; Gerek,Ö.N.; Bilge,A.; Özkan,K.; Computer EngineeringThis study presents a novel Quaternion-based link prediction method to be used in different recommendation systems. The method performs Quaternion algebra-based computations while making use of expressive and wide-ranged learning properties of the Hamilton products. The proposed key capabilities rely on link prediction to boost performance in top-N recommendation tasks. According to the achieved experimental results, the proposed method allows for highly improved performance according to three quality measurements: (i) hits rate, (ii) coverage, and (iii) novelty; when applied to two datasets, namely the Movielens and Hetrec datasets. To assess the flexibility level of the proposed algorithm in terms of incorporating alternative sources of information, further wide-scale tests are carried out on three subsets of the Amazon dataset. Hence, the effectiveness of Quaternion algebra in graph-based recommendation algorithms is verified. The algorithms suggested here are further enhanced using similarity and dissimilarity factors between users and items, as well as ‘like’ and ‘dislike’ relationships between users and items. It is observed that this approach is adaptable by incorporating different information sources and can successfully overcome the drawbacks of conventional graph-based recommender systems. It is argued that the proposed novel idea of Quaternion-based link prediction method stands as a superior alternative to existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Book Part Citation Count: 2A Multi Source Graph-Based Hybrid Recommendation Algorithm(Springer Science and Business Media Deutschland GmbH, 2021) Kurt, Zühal; Gerek,Ö.N.; Bilge,A.; Özkan,K.; Computer EngineeringImages that widely exist on e-commerce sites, social networks, and many other applications are one of the most important information resources integrated into the recently deployed image-based recommender systems. In the latest studies, researchers have jointly considered ratings and images to generate recommendations, many of which are still restricted to limited information sources, sources namely, ratings with another input data, or which require the pre-existence of domain knowledge to generate recommendations. In this paper, a new graph-based hybrid framework is introduced to generate recommendations and overcome these challenges. Firstly, a simple overview of the framework is provided and, then, two different information sources (visual images and numerical ratings) are utilized to describe how the proposed framework can be developed in practice. Furthermore, the users’ visual preferences are determined based on which item they have already purchased. Then, each user is represented as a visual feature vector. Finally, the similarity factors between the users or items are evaluated from the user visual-feature or item visual-feature matrices, to be included the proposed algorithm for more efficiency. The proposed hybrid recommendation method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results demonstrate the superior performance of the proposed appraoch using three quality measurements - hit-ratio, recall, and precision - on the three subsets of the Amazon dataset, as well as its flexibility to incorporate different information sources. Finally, it is concluded that hybrid recommendation algorithms that use the integration of multiple types of input data perform better than previous recommendation algorithms that only utilize one type of input data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Article Citation Count: 7Recurrent neural networks for spam E-mail classification on an agglutinative language(Ismail Saritas, 2020) Kurt, Zühal; Kurt,Z.; Anagun,Y.; Ozkan,K.; Computer EngineeringIn this study, we have provided an alternative solution to spam and legitimate email classification problem. The different deep learning architectures are applied on two feature selection methods, including the Mutual Information (MI) and Weighted Mutual Information (WMI). Firstly, feature selection methods including WMI and MI are applied to reduce number of selected terms. Secondly, the feature vectors are constructed with concept of the bag-of-words (BoW) model. Finally, the performance of system is analyzed with using Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BILSTM) models. After experimental simulations, we have observed that there is a competition between detection results of using WMI and MI when commented with accuracy rates for the agglutinative language, namely Turkish. The experimental scores show that the LSTM and BILSTM give 100% accuracy scores when combined with MI or WMI, for spam and legitimate emails. However, for particular cross-validation, the performance WMI is higher than MI features in terms e-mail grouping. It turns out that WMI and MI with deep learning architectures seem more robust to spam email detection when considering the high detection scores. © 2020, Ismail Saritas. All rights reserved.