Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

Gong, Jianya; Zhao, Yi; Chen, Shuai; Wang, Hongfei; Du, Linfeng; Wang, Shuli; Bhuiyan, Zakirul Alam; Peng, Hao; Du, Bowen

doi:10.1109/access.2019.2958599

Cited by 18 publications

(6 citation statements)

References 39 publications

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“…Recommendation systems are a type of technology that can be used in a variety of ways, such as suggesting movies [65], [68], [69] or products to customers, recommending articles to readers, or suggesting friends on POI [17], [70] & social media [71]. These systems can differ significantly in terms of their purpose, the algorithms and techniques they use, their functionality, and how they are implemented.…”

Section: B Classification Of Recommendation System Categoriesmentioning

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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Section: B Classification Of Recommendation System Categoriesmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This is done by fusing the factors of sentiment similarity and geographical distance. Gong et al [93] recommend friends for a user by applying a deep learning technique. Friends recommendation combines static attributes (e.g., the geographical location), dynamic behaviors (e.g., liking), and network structures (e.g., social relations).…”

Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%

“…[84] Hybrid [24], [31], [33], [34], [37], [56], [74], [81], [85], [86], [93] [24], [40], [56], [61], [81], [85], [86], [89] Over the last years, CARSs have been a focus of attention for various research communities where enormous studies were proposed to enhance their efficiency. In this section, we briefly present context-based recommender system approaches and techniques and highlight recent developments in modern state-of-the-art techniques, including deep learning and hybrid techniques.…”

Section: Sparsitymentioning

confidence: 99%

“…Table III shows the distribution of studies based on the different techniques that are used in recommendation approaches. Techniques References Bayesian probability [80], [90] Bipartite graphs embedding [84] Clustering [30], [60], [70], [91], [107] Decision tree [26], [38], [83] Deep neural networks [35], [56], [59], [65], [71], [72], [73], [76], [93], [98], [99], [100], [108] Distance-based method [88], [89] Fuzzy clustering [113] K Nearest Neighbor [24], [25], [44], [54], [75]…”

Section: Sparsitymentioning

confidence: 99%

“…The model uses CNN and RNN techniques to learn article contextual properties and sequential temporal patterns in streams of clicks, respectively. The framework presented in [93] uses the large-scale information network embedding algorithm to combine attention and network embedding in three phases. Firstly, it produces semantic topics and forms static attribute features by utilizing the LDA algorithm.…”

Section: ) Hybrid Approachmentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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Online Education Resource Recommendation System of International Finance Course Based on Preference Data Collection

Qiao¹

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

Cited by 18 publications

References 39 publications

Deep Learning-Based Recommendation System: Systematic Review and Classification

Deep Learning-Based Recommendation System: Systematic Review and Classification

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Online Education Resource Recommendation System of International Finance Course Based on Preference Data Collection

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