An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS)

Al-Sbou, Ahed Mleih; Rahim, N. H. Abd

doi:10.11591/ijeecs.v30.i1.pp481-490

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Applied in dimensionality reduction, feature learning, and denoising tasks across various domains [26].…”

Section: Autoencodermentioning

confidence: 99%

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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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“…Applied in dimensionality reduction, feature learning, and denoising tasks across various domains [26].…”

Section: Autoencodermentioning

confidence: 99%

“…However, recommendation systems face challenges such as data sparsity, cold start, and the need for collecting past user feedback [26], [27]. Researchers are developing more effective recommendation algorithms to overcome these challenges and improve accuracy and user satisfaction [28], [29].…”

mentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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“…By utilizing DL algorithms, it is possible to extract latent features from raw data effectively, and this approach is becoming increasingly popular in MCRS to tackle issues of sparsity and scalability [12]. Among the DL techniques, the autoencoder, in particular, has gained significant attention for its application in recommendation systems [13,14]. Autoencoders are neural network architectures that can learn to encode input data into a lower-dimensional latent space and then decode it back to reconstruct the original input.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

2023

IJIES

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Recommender systems are extensively employed across various domains, encompassing movies, hotels, and restaurants, with the aim of delivering personalized recommendations to users. However, the conventional approach of relying solely on a single rating may fall short in comprehending the underlying factors contributing to a user's overall rating. As a remedy, multi-criteria recommender systems (MCRSs) have emerged as an alternative paradigm, allowing users to rate items based on multiple dimensions. The primary challenge lies in predicting the overall rating considering a user's multi-criteria scores. In this study, we propose a MCRS that leverages a multicriteria decision-making method, which takes into account the interdependence among criteria. To this end, the weight assigned to each criterion is calculated using the correlation coefficient and standard deviation (CCSD) approach. Furthermore, we employ a deep autoencoder to capture intricate nonlinear relationships and generate recommendations of heightened accuracy. Evaluating our proposed method on the well-known MovieLens and TripAdvisor datasets, we demonstrate its superior performance compared to several single and multicriteria recommender systems. The results highlight the effectiveness of our approach in capturing pertinent aspects and achieving lower prediction errors, showcasing its potential in enhancing recommendation quality and personalization.

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