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An Application-oriented Review of Deep Learning in Recommender Systems

Jyoti Shokeen, Chhavi Rana
<span title="2019-05-08">2019</span> <i title="MECS Publisher"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/koacvjimprbbvhwasrewxdbiuy" style="color: black;">International Journal of Intelligent Systems and Applications</a> </i> &nbsp;
This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications.  ...  The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.  ...  DEEP LEARNING IN SOCIAL RECOMMENDER SYSTEMS The combination of social networks with RS is termed as social recommender system [79] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijisa.2019.05.06">doi:10.5815/ijisa.2019.05.06</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/67fgexfbfjh2no5b3phvohbole">fatcat:67fgexfbfjh2no5b3phvohbole</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200215010748/http://www.mecs-press.org/ijisa/ijisa-v11-n5/IJISA-V11-N5-6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/10/fb/10fbae881ed34cef0438beca604bb99ac57ed030.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijisa.2019.05.06"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
<span title="2020-10-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We focus on the work based on deep learning techniques, an emerging area in the recommendation research.  ...  This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that  ...  Using Latent Representation for Recommendation Another idea for utilizing autoencoder for social-aware recommendation is to combine the learned latent representation with other methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03774v2">arXiv:2004.03774v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7mehir7hbbzpemw3q5fkby5ty">fatcat:q7mehir7hbbzpemw3q5fkby5ty</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201010114329/https://arxiv.org/pdf/2004.03774v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03774v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey of Deep Learning Approaches for Recommendation Systems

Jun Yi Liu
<span title="">2018</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
This paper provides a survey of recommendation systems, which focuses on deep learning approaches and the system of applications.  ...  As deep learning develops, the application of deep neural network in related research is increasingly prevalent.  ...  Depending on the above algorithms, many researchers combined the conventional recommender systems with the deep learning algorithms. For example, Oh et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1087/6/062022">doi:10.1088/1742-6596/1087/6/062022</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2mep7pcyvrdvldwq7z3ts6w6gm">fatcat:2mep7pcyvrdvldwq7z3ts6w6gm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430203518/https://iopscience.iop.org/article/10.1088/1742-6596/1087/6/062022/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/34/5d/345ded3df98d4b5fbcfd4ca893645e0ea2abc32f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1087/6/062022"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>

Recommendation system using a deep learning and graph analysis approach [article]

Mahdi Kherad, Amir Jalaly Bidgoly
<span title="2021-07-13">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold-start and sparsity  ...  Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting.  ...  Deep Modeling of Social Relations for recommendation method uses a deep neural network to learn the representations of each user from social relationships that integrate with PMF to predict ratings.   ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.08100v8">arXiv:2004.08100v8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/olpgxe5u5zg3tphqbofgdfilmu">fatcat:olpgxe5u5zg3tphqbofgdfilmu</a> </span>
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Hybrid Collaborative Recommendation via Dual-Autoencoder

Bingbing Dong, Yi Zhu, Lei Li, Xindong Wu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
With deep learning achieved good performance in representation learning, the autoencoder model is widely applied in recommendation systems for the advantages of fast convergence and no label requirement  ...  INDEX TERMS Recommendation system, matrix factorization, semi-autoencoder.  ...  With the superiority of deep learning in feature learning, recommendation algorithms based on deep learning spring up.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2979255">doi:10.1109/access.2020.2979255</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xtlagg46lvarlnckeib6dy5nxm">fatcat:xtlagg46lvarlnckeib6dy5nxm</a> </span>
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RECOMMENDER SYSTEMS BASED ON DEEP LEARNING

Xushbaqov Sherzod, Khamraev Mansur, Bakhtiyorova Mokhiruy
<span title="2022-04-23">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Next, we present deep learning-based hybrid recommender systems and deep learning in social network-based recommender systems. Finally, we describe deep learning in contextaware recommender systems.  ...  In this article, we describe deep learning-based recommender systems. First, we introduce deep learning in content-based recommender systems.  ...  Compared with traditional recommender systems, deep learning-based recommender systems can use deep learning technique to automatically learn the latent features of user and item by integrating various  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6473945">doi:10.5281/zenodo.6473945</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kwzv2uc25ngjzg5sxver7yp2ke">fatcat:kwzv2uc25ngjzg5sxver7yp2ke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220423153052/https://zenodo.org/record/6473945/files/Xushbaqov%20Sherzod%2C.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/97/fb97aa39e086f848b1006c0b1ad9f38d9571ebdb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6473945"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

Syed Falahuddin Quadri, Xiaoyu Li, Desheng Zheng, Muhammad Umar Aftab, Yiming Huang
<span title="">2019</span> <i title="Computers, Materials and Continua (Tech Science Press)"> Journal on Big Data </i> &nbsp;
Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems  ...  as AutoEncoders.  ...  Figure 1: Illustration of a fully connected autoencoder with 1 hidden layer Collaborative Filtering and Feature Representation Learning are successful applications of autoencoders in the recommendation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/jbd.2019.05899">doi:10.32604/jbd.2019.05899</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5w6sukxjqre7hcrcatnnddfova">fatcat:5w6sukxjqre7hcrcatnnddfova</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320015417/http://tsp.techscience.com/uploads/attached/file/20190329/20190329020631_43272.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/96/ea/96ea0ff467d6066e06fe5e6e17427e79b2e1385d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/jbd.2019.05899"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
<span title="2021-12-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  and practitioners working on recommender systems.  ...  The facial attributes are then fed into a deep learning based recommendation system for personalized makeup synthesis [124] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.13030v3">arXiv:2104.13030v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7bzwaxcarrgbhe36teik2rhl6e">fatcat:7bzwaxcarrgbhe36teik2rhl6e</a> </span>
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A Review for Recommender System Models and Deep Learning

F. Nagy, A. Haroun, Hatem Abdel-Kader, Arabi Keshk
<span title="2021-12-01">2021</span> <i title="Egypts Presidential Specialized Council for Education and Scientific Research"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p2xb6cqe3vgdvoxuie26a77bmy" style="color: black;">IJCI. International Journal of Computers and Information</a> </i> &nbsp;
traditional technology, how deep learning-based recommendation systems works, deep learning for recommendations and open problems and the novel research trends on this field.  ...  In this paper we introduce an overview for the traditional recommendation systems models, the recommendation systems advantages and shortcoming, the recommendation systems challenges, common deep learning  ...  , common deep learning traditional technology, how does deep learning-based recommendation systems work, deep learning for recommendations and open problems and the novel research trends on this research  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21608/ijci.2021.207864">doi:10.21608/ijci.2021.207864</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hdwzp3o4djcsdo6ubqfkdmu3o4">fatcat:hdwzp3o4djcsdo6ubqfkdmu3o4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220117021214/https://ijci.journals.ekb.eg/article_207864_d767aea4b612492d52156288691fa256.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d0/cd/d0cd2ef94e4d459e8e6eaad77aaaae570dae2136.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21608/ijci.2021.207864"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Neural Collaborative Autoencoder for Recommendation with Co-occurrence Embedding

Wei Zeng, Jiwei Qin, Chunting Wei
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
To solve these two challenges, we design a model named the Neural Collaborative Autoencoder for Recommendation with Co-occurrence Embedding (NCAR), which is divided into two parts: (1) the user co-occurrence  ...  interaction behavior for collaborative recommendation.  ...  In Autoencoder-based Recommendation Systems (RSs), autoencoder (AE) helps the recommendation systems better understand user and item by learning the nonlinear useritem relationship efficiently.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3133628">doi:10.1109/access.2021.3133628</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/orzxvqybzvdennee63trnhb2ey">fatcat:orzxvqybzvdennee63trnhb2ey</a> </span>
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Deep Learning based Recommender System: A Survey and New Perspectives [article]

Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay
<span title="2018-09-04">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.  ...  property of learning feature representations from scratch.  ...  Feature Representation Learning with Autoencoder. Autoencoder is a class of powerful feature representation learning approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.07435v6">arXiv:1707.07435v6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2q2dbfy2jvdydhbrmmbyrzctnq">fatcat:2q2dbfy2jvdydhbrmmbyrzctnq</a> </span>
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Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Liangtian Wan, Feng Xia, Xiangjie Kong, Ching-Hsien Hsu, Runhe Huang, Jianhua Ma
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kxofu2xuvjhazhdfub6fxumwhu" style="color: black;">IEEE Transactions on Network Science and Engineering</a> </i> &nbsp;
Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied.  ...  Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix  ...  The recommendation performance has been improved significantly when deep learning is embedded in the recommendation systems [30] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnse.2020.3044035">doi:10.1109/tnse.2020.3044035</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jfrirag34zcuvdjyda7tqo556i">fatcat:jfrirag34zcuvdjyda7tqo556i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717175655/https://ieeexplore.ieee.org/ielx7/6488902/9380808/09291430.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8e/31/8e3127ce2bc08bc5e8fe697941e25d166a1215a8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnse.2020.3044035"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

Sule BİRİM
<span title="2021-07-31">2021</span> <i title="International Journal of Informatics Technologies"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/heqikokm35ervdsppquidtxm7i" style="color: black;">Bilişim Teknolojileri Dergisi</a> </i> &nbsp;
With this aim, this study proposes deep autoencoders methodology to recommend services and applications to users based on the devices they own.  ...  The results showed that deep autoencoders outperformed the state-of-the-art recommendation methods.  ...  The success of deep learning grabbed the attention of recommender system appliers [22] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17671/gazibtd.685500">doi:10.17671/gazibtd.685500</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ka2ch5s35zbepo2j6z5dlzt3ke">fatcat:ka2ch5s35zbepo2j6z5dlzt3ke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210806034210/https://dergipark.org.tr/en/download/article-file/958774" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/ae/d4ae9f9ddbb296529af938630f43026093f409d7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17671/gazibtd.685500"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation [article]

Zhenghua Xu, Cheng Chen, Thomas Lukasiewicz, Yishu Miao
<span title="2017-08-12">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web.  ...  recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization.  ...  [16] propose a deep-semantic model called DSPR which utilizes deep neural networks to model abstract and recommendationoriented representations for social tags.  ... 
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Collaborative Deep Forest Learning for Recommender Systems

Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Recommender systems, social networks, deep learning, collaborative filtering, representational learning.  ...  First, representation learning is employed on the rating matrix to extract the latent social features.  ...  Deep learning enables multi-layered computational models to learn data representations with multiple abstraction levels [18] .  ... 
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