Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

Bathla, Gourav; Rani, Rinkle; Aggarwal, Himanshu

doi:10.14569/ijacsa.2018.091049

Cited by 4 publications

(2 citation statements)

References 15 publications

(21 reference statements)

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“…( 2015 ), (Vlachos and Riedel 2014 ) PolitiFact Fake news detection 488 2 Bathla et al. ( 2018 ), Horne and Adali ( 2017 ) Weibo Rumor detection 816 2 Ma et al. ( 2016 ) YelpChi Fake review detection 67K 2 Mukherjee et al.…”

Section: Discussion With Open Issues and Future Researchmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Discussion With Open Issues and Future Researchmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

View full text Add to dashboard Cite

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“…As data becomes more readily available, recommendation systems are gaining popularity in e-commerce. In today's business world, data-driven decisions are crucial, and many companies are incorporating recommendation system features into their websites and apps to enhance user experience and increase revenue [1]- [5]. The purpose of recommendation systems is to provide personalized and relevant suggestions to users based on their past behavior, preferences, and interests [6]- [9], and to solve the problem of information overload in various domains such as e-commerce [10]- [12], e-learning [13]- [15], social networks [16]- [21], and enter-tainment [22]- [25].…”

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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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