Evolutionary neural networks for improving the prediction performance of recommender systems

Seref, Berna; Bostancı, Erkan; Güzel, Mehmet Serdar

doi:10.3906/elk-2004-138

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The effectiveness of the models on the ML-1M dataset was measured using two evaluation metrics, MAE and RMSE. The baseline models consisted of Item-based model [28], SVD model [29], FunkSVD model [28], StaTNA model [30], MLP-Genetic algorithm [31], Generalized feed-forward network [31], ILIG model [32], FM model [33] with AFM model [33]. The model used in this paper is TwinBERT.…”

Section: Comparison Of Modelsmentioning

confidence: 99%

Recommendation Approach Based on TwinBERT Model

Luo

2024

HSET

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In order to provide more personalized recommendation services for users as well as improve the platform's traffic and user satisfaction, a recommendation method based on the TwinBERT model has been proposed. As a matter of fact, this method combines not only the basic information of users and items but also user comments or item text information to predict users' behaviors and make more accurate recommendations. The text information in the dataset is encoded in sentences using the BERT model, and the encoded text features are fused with other features to obtain a representation of user and item embedding vectors. These vectors are then used to predict the user's rating of the item and recommend relevant items for the user. The proposed method was tested on the Amazon review dataset and the MovieLens dataset, and the experimental results showed that it improved over traditional recommendation algorithms in terms of accuracy and efficiency.

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Section: Comparison Of Modelsmentioning

confidence: 99%

Recommendation Approach Based on TwinBERT Model

Luo

2024

HSET

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“…The sessionbased category comprises terms related to session-based recommendation systems [106], [227], [228]. The optimization category contains terms related to optimization techniques used in recommendation systems, such as genetic algorithm [229]- [231] and Taylor series [232]. The factor analysis category includes terms related to factor analysis techniques for recommendation systems, such as factor analysis [233], [234] and factor model [18], [59], [235], [236].…”

Section: N: Hybrid Approaches For Comprehensive Solutionsmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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“…These methods were then assessed based on their SSIM, PNSR and MSE in a quantitative evaluation. A qualitative evaluation was also performed on the results to ensure that the interpolation approach yielded visually appealing images (Bostanci, 2014 andSeref et. al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Interpolation-Based Image In-Painting Approaches

Zor¹,

Bostancı²,

Güzel³

et al. 2022

Advanced Sensing in Image Processing and IoT

Self Cite

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Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image inpainting in the literature. Errors and noise generated on the colour and grayscale formats of some of the commonly used standard images in the literature were corrected by using Cubic, Kriging, Radial based function and High dimensional model representation approaches and the results were compared using standard image comparison criteria, namely, PSNR (peak signalto-noise ratio), SSIM (Structural SIMilarity), Mean Square Error (MSE). According to the results obtained from the study, the absolute superiority of the methods against each other was not observed. However, Kriging and RBF interpolation give better results both for numerical data and visual evaluation for image in-painting problems with large area losses.

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Evolutionary neural networks for improving the prediction performance of recommender systems

Cited by 8 publications

References 38 publications

Recommendation Approach Based on TwinBERT Model

Recommendation Approach Based on TwinBERT Model

Deep Learning-Based Recommendation System: Systematic Review and Classification

Analysis of Interpolation-Based Image In-Painting Approaches

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