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The use of machine learning algorithms in recommender systems: A systematic review
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“The mathematics of recommender systems [20] and interpolation capabilities of machine learning algorithms will enable proper recommendations on making the secondary surveys cheaper with a minor loss in the value of acquired information.”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [20]
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MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476 · cited 1×
“The MapReduce algorithm [17], which became popular in mid-1990s, is a programming model suited for distributed and parallel computation.”
MethodJain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925 · cited 1×
“Today, there is a plethora of ML algorithms (k-nearest neighbor [48], clustering [28], Bayes network [22], to name a few types), which are used in applications that range from vacuum cleaner robots [13] and assistance for disabled people [30] to pattern recognition in images [63], or self-driving…”
BackgroundAdomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,539 · cited 2×
“Recommender systems (RS) are used to help users find new items or services, such as books, music, transportation or even people, based on information about the user, or the recommended item [2].”
BackgroundSoftware engineering: A practitioner's approach
(1983) · Advances in Engineering Software (1978) · doi:10.1016/0141-1195(83)90118-3 · Cited by 8,227
(2004) · Cited by 5,198
The Hadoop Distributed File System
Shvachko, K. V., Kuang, H., Radia, S., & Chansler, R. J. (2010) · Digest of papers - IEEE Symposium on Mass Storage Systems · doi:10.1109/msst.2010.5496972 · Cited by 4,911 · cited 1×
“The proposed architecture that provides on demand tools for analysis uses the storage technologies HDFS (Hadoop Distributed File System) (Shvachko et al., 2010) and HBase6 for persistence logs and structured information about the execution and predictions.”
MethodFriedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742 · cited 1×
“Today, there is a plethora of ML algorithms (k-nearest neighbor [48], clustering [28], Bayes network [22], to name a few types), which are used in applications that range from vacuum cleaner robots [13] and assistance for disabled people [30] to pattern recognition in images [63], or self-driving…”
BackgroundSupervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192
Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. B. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,071 · cited 1×
“The field of RS has its origins in the mid-1990s with the introduction of Tapestry [23], the first RS.”
BackgroundUnderstanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Research works printed but not linked: 14
- Aouay, 2014, 2014, 523
- Bagchi, 2015, 50, 229
- Burhans, 2004, 1, 82
- Jin, 2005, 1, 213
- Jun, 2005, 3512, 349
- Li, 2008, 3, 1626
- Liu, 2011, 4, 1909
- Montañés, 2009, 497, 173
- Murfi, 2009, 497, 201
- Nicol, 2014, 2, 1390
- Pecli, 2015, 1, 295
- Yeh, 2010, 1, 209
- Zhang, 2007, 6, 3506
- Zhang, 2007, 7, 4113
Cited by
Sorted by citations
Items 601–700 of 742
The use of Recommender Systems in web technology and an in-depth analysis of Cold State problemPreprint
Selimi, D., & Nuci, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04780
“Kiseleva et al.[10] stated that there are different ways to define the users who become inactive: 1.”
BackgroundA Multilevel Intelligent Assistant for Multilevel Social Network Analysis
Tianxing, M., Zhukova, N. A., & Tsochev, G. (2020) · IEEE International Conference on Intelligent Systems · doi:10.1109/is48319.2020.9199840
“Machine learning techniques are used for mining knowledge in various areas, such as the recommendation system [11], user psychoanalysis [12, 13], etc.”
BackgroundHow Do We "Raise" Ethically Minded Computer Students?
Milonas, E. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--34730
“machine learning algorithms cannot reason [18].”
BackgroundRecommender Vision: um sistema de recomendação para o comércio baseado em Visão Computacional
Seminotti, M. A., & Rieder, R. (2020) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2020.12998
“nho, como precisão, escalabilidade e inicialização a frio [3].”
Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care
Zhuang, K.-X., & Hsu, I. (2020) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.2020100103
Actor’s knowledge massive identification in the learning management system
Touimi, Y. B., Hadioui, A., Faddouli, N.-E. E., & Bennani, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823410-5.00009-7
Personalized Hybrid Recommender System with Average Time Intervals
Lee, M., & Cha, S. (2021) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet51464.2021.9456260
“There are supervised, unsupervised, semi-supervised, and reinforcement learning methods [18].”
BackgroundImproving Recommender Systems by Using Time-Weighted Sentiment Analysis
Theuerkauf, R., Seyffahrt, T., & Peters, R. (2021) · International Conference on E-Commerce, E-Business and E-Government (ICEEG) · doi:10.1145/3466029.3466057
“At the same time, recommender systems are based on an array of algorithms, as 15 systems using machine learning approaches have shown [17].”
BackgroundMethodShayboun, M., Koch, C., & Kifokeris, D. (2021) · Chalmers Research (Chalmers University of Technology)
A Feature Based Fitness Cautionary System for Heavy Internet Users by using Deep Learning
Sinha, V. K., & Rishu (2021) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat52182.2021.9587522
Pirutin, S. K., Туровецкий, В. Б., & Kudriashov, I. B. (2009) · PubMed
Research of the applicability of machine learning methods for assessment of departments’ performance
Mukanova, A. R., & Otsokov, S. (2021) · Informatics and Education · doi:10.32517/0234-0453-2021-36-8-41-51
Use of Machine Learning in Application of Artificial Intelligence
Jain, A., Singh, A., & Jain, I. (2021) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart52563.2021.9676318
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Parbat, T. T., Benhal, R. S., Jain, H., & Musale, V. (2021) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit217674
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Jajoo, P., & Mittal, D. (2021) · SKIT Research Journal · doi:10.47904/ijskit.11.2.2021.31-36
Rutkowski, T. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-75521-8_1
State of the Art Recommendation Approaches
Rawat, B., & Dwivedi, S. K. (2019) · Natural Language Processing · doi:10.4018/978-1-7998-0951-7.ch075
Nuevas perspectivas en el estudio de amenazas persistentes avanzadas
Bonilla, S. Q. (2021) · doi:10.14201/gredos.149395
The Use of Intelligent Models in Decision Support
Sànchez–Marrè, M. (2022) · Springer eBooks · doi:10.1007/978-3-030-87790-3_7
Data Security in Enterprise Management Under Big Data Technology
Zhang, C. (2021) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie53922.2021.00200
“After artificial intelligence optimization, it can greatly improve the performance and work efficiency of the Internet of things equipment [3].”
BackgroundBasic Scientific and Clinical Applications
Chetty, M. S. R., & Babu, C. V. K. (2022) · Wiley eBooks · doi:10.1002/9781119792376.ch11
Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study
Wu, J.-Y., & Liu, P.-C. (2022) · doi:10.5121/csit.2022.121012
“ML algorithms, which consist of supervised, unsupervised and reinforcement learning, are that computers can simulate a human learning, identify and acquire knowledge from the real world and can enhance its performance based on the obtained knowledge on some tasks [12].”
BackgroundAn adaptive neuro-fuzzy inference for blockchain-based smart job recommendation system
Sujatha, R., Ephzibah, E. P., & Chatterjee, J. M. (2022) · International Journal of Information and Decision Sciences · doi:10.1504/ijids.2022.10047264
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
Nazeer, I., Rashid, M. U., Gupta, S. K., & Kumar, A. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch010
Recommendation System with Artificial Intelligence for Welding Quality Improvement
Tran, T.-T., Jang, H., & Vu, A. Q. N. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-15063-0_21
Semantic Web Recommender System over Different Operating Platforms
Sharif, H. K., & Faraj, K. (2022) · UHD Journal of Science and Technology · doi:10.21928/uhdjst.v6n2y2022.pp19-23
“However, our proposed system is SW with the cosine similarity is a method and part of content-based algorithm (CBA) for filtering all titlemovie in dataset of movie-Lens [2].”
MethodVeterinary Medication Prescription Analysis: A Machine Learning Approach
Midhun, M., & Devananda, M. (2022) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis54991.2022.9885534
“Machine learning algorithms make a recommendation system more intelligent than rule-based systems [16].”
BackgroundAprendizaje de idiomas usando Machine Learning: una revisión sistemática
Gamboa-Cruzado, J., Huamani-Jeri, J., Najarro-Buitron, A., Sánchez, A. H., Chaca, M. D., & Zegarra, I. E. H. (2022) · Apuntes Universitarios · doi:10.17162/au.v12i4.1249
Mitigating Judgmental Fallacies with Social Robot Advisors
Polakow, T., Teodorescu, A. R., Busemeyer, J. R., & Gordon, G. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900698
“This mechanism becomes more useful as different ranking methods appear [4], multiple recommendation systems are available [5] and social robots become advisors.”
BackgroundXGBoost meets TabNet in Predicting the Costs of Forwarding Contracts
Lewandowska, A. (2022) · Annals of Computer Science and Information Systems · doi:10.15439/2022f294
“Recommender systems [3], [4] may suggest to customers what products they may be interested in, thereby increasing the chances of buying a product.”
BackgroundFormal Methods in FCA and Big Data
López-Rodríguez, D., Muñoz‐Velasco, E., & Ojeda‐Aciego, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-93278-7_9
Intelligent Recommender System for Spatial Analysis
Belyakov, S. L., Bozhenyuk, A. V., Dolgiy, I., & Владимировна, К. М. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-19620-1_9
Efficient Classification with Counterfactual Reasoning and Active Learning
Mohammed, A., Nguyen, D., Duong, B., & Nguyen, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_3
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Nunes, E. C., Barbosa, J., Alves, P. A., Franco, T., & Silva, A. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23236-7_33
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Gajjar, S., & Rahevar, M. L. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-7346-8_54
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Kale, A., Kadri, U., Kamble, J., Thorat, M., Vijayan, P. M., Badgujar, K. D., & Kharade, P. A. (2023) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/2352096516666230512145824
An Intelligent Long-Term Care Information Platform using Machine Learning and Semantic WebPreprint
Hsu, I., & Chen, S.-H. (2023) · Research Square · doi:10.21203/rs.3.rs-2986151/v1
Huang, X., & Liang, M. (2023) · Soft Computing · doi:10.1007/s00500-023-08869-4
Retractedجابر, م. م. (2023) · مجلة کلية التربية · doi:10.21608/jfe.2023.311805
Study of Machine Learning for Recommendation Systems
Deshpande, T., Chavan, K., & Mangrulkar, R. S. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815136746123010004
Intelligent Design of Images of Engineering Networks for Spatial Analysis
Belyakov, S. L., Bozhenyuk, A. V., Владимировна, К. М., & Rozenberg, I. N. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-43792-2_28
Advanced Persistent Threats Detection Through Machine Learning Techniques
Full Professor, I. S. D. T. A. (., Brandão, P. R., Rodrigues, J. I. G., & Master's Student in Informatics Instituto Superior de Tecnologias Avançadas (ISTEC), L. (2023) · Journal of Material Sciences and Engineering Technology · doi:10.61440/jmset.2023.v1.12
(2023) · Computer Science Engineering and Technology · doi:10.46632/cset/1/3/2
Recommending Related Research Papers by Topic Modelling
van Heerden, J., Haskins, B., & Botha, R. A. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389606
“The improvement of recommendation quality can be attained through the application of clustering techniques on web articles and other textual features [16].”
BackgroundNonlinear dynamic feature fusion in Wearable Sensor-Based Human Activity Inference
Liao, T.-Z., Liu, W.-H., Lv, W., Zou, T., Yan, X.-M., & Lin, X. (2023) · International Conference on Health Big Data and Intelligent Healthcare (ICHIH) · doi:10.1109/ichih60370.2023.10396635
“For a long time, machine learning has been widely used in HAR scenarios such as Hu [1] and others using Random Forest, Xiao [2] using Bayesian Networks, Sok [3] using Markov Models, and Portugal [4] using Support Vector Machine (SVM) for solving the problem of human activity recognition.”
MethodEdu Venture Application: Bridging Honorary Teacher with Skill-BasedProject
Karisma, C. R. R. C., Vincent, V., Kesuma, M. A., Kurniawan, D. K., & Kudori, D. S. (2023) · International Conference on Information Technology and Computing (ICITCOM) · doi:10.1109/icitcom60176.2023.10442931
“A standout feature is its 'Skill-Based Project Recommendation', harnessing advanced machine learning algorithms, the platform analyzes an educator's profile, past experiences, and unique skill set to suggest projects that align perfectly with their expertise[9], [10].”
BackgroundTHE IMPACT OF ARTIFICIAL INTELLIGENCE ON UNIVERSITY MAJOR
Akhras, C., & Nicolian, N. (2024) · International journal of business management and economic review · doi:10.35409/ijbmer.2024.3548
Recommender Systems and Analysis of Large-Scale Online Social Networks
Marappan, R., & Saraswatikaniga, R. (2023) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit60255.2023.10466167
“The overfitting problem is reduced through SVD [4].”
BackgroundArtajaya, H., Julieta, Giancarlos, J., Moniaga, J. V., & Chowanda, A. (2023) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai-winter61682.2023.00055
“Usually, this system is used to help user to find items or services based on the user’s information [5].”
Zokaei, Z., Shahrokhzadeh, B., Farahani, M., & Hosseinalizdeh, S. (2024) · doi:10.1109/qicar61538.2024.10496631
McDermid, J. A., Jia, Y., & Habli, I. (2024) · Cambridge University Press eBooks · doi:10.1017/9781108980197.002
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Aisyah, T., Indriasari, M., Djuwitaningrum, E. R., Widyastuti, W., Indraswati, T. D., Sulistyoningsih, M. M. G. D. R., Alimuddin, Arafiyah, R., Herlambang, M. B., Setiawan, A., et al. (2024) · doi:10.31237/osf.io/ydfrv
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Gärtner, C. (2024) · Springer eBooks · doi:10.1007/978-3-658-44904-9_4
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Azeroual, O. (2024) · Information - Wissenschaft & Praxis · doi:10.1515/iwp-2024-2014
Bitirim, S., & Ertuğrul, D. Ç. (2024) · Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.17780/ksujes.1410926
Performance Analysis of Machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique
Kumar, B. S., Yadav, P. P., & Prasad, P. P. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-8031-0_15
On the Employment of Machine Learning for Recommending Refactorings: A Systematic Literature Review
Armijo, G. A., Martin, D., Durelli, R. S., & de Camargo, V. V. (2024) · Simpósio Brasileiro de Engenharia de Software (SBES) · doi:10.5753/sbes.2024.3436
Anwar, N., Adhy, D. R., Maesaroh, S., Hadi, M. A., Hermawan, R., Rifqi, M., Tugiman, T., & Jumarlis, M. (2024) · AIP conference proceedings · doi:10.1063/5.0225195
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Xiong, Q., Dong, H., Sohrabi, N., Tari, Z., & Moustafa, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5042112
Mada, S. P. B., Tata, R., Thondapu, S. T. S. R., & Saleti, S. (2024) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices62143.2024.10779628
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Mohamedhen, A. S., Arfaoui, N., Ejbali, R., & Nanne, M. F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71429-0_2
Yang, J., Wang, X., Ni, Q., Liu, Y., Wang, J., & Wang, F.-Y. (2024) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/iccsi62669.2024.10799393 · Cited by 1
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“Additionally, this research aims to explore the effectiveness of different collaborative filtering techniques under varying conditions and provide insights into the performance of machine learning algorithms in recommendation systems [1][2].”
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“A strong focus on collaborative filtering, supported by various algorithms like clustering, support vector machines (SVM), deep learning (DL), and Bayesian methods, indicates an increasing interest in harnessing big data’s potential to enhance recommendation quality and relevance [6,7].”
Mebarkia, K., & Reffad, A. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121853
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Briciu, V.-A., Catargiu, A.-C., & Briciu, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7693-5.ch005
WANG, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5123514
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“From the perspective of core technology, the core technology of artificial intelligence (AI) includes a variety of algorithms, such as machine learning [3], deep learning [4] and natural language processing [5].”
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Li, Z., Lv, Y., Zhang, L., Liang, W., & Tang, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5168746
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“It is common for hybrid systems to add more complexity, which makes debugging and improvement more difficult [24].”
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“Their business value has been demonstrated by platforms such as Google, YouTube and Net-flix, where recommendations directly influence user satisfaction, retention, and revenue [14, 15].”
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“Fuzzy C-means algorithms allow for shared membership across clusters, enhancing pattern detection and addressing cold-start problems [12], [13].”
Machine Learning—Book Recommendation Using KNN
Bokhare, A., Pabalkar, V., & Salunkhe, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4136-9_7
Methods and challenges in job recommendation systems: A review
Hasan, M. R., Goh, H.-N., Ting, C., Quek, A., & Haque, R. (2025) · AIP conference proceedings · doi:10.1063/5.0299300
Beyond the List: A Framework for the Design of Next-Generation MEDLINE Search Tools
Zhurov, V., Sedig, K., & Milani, M. (2025) · Data · doi:10.3390/data10100167
“These models can be trained with or without explicit human-provided labels (i.e., supervised or unsupervised learning) and can even improve iteratively by learning from user interactions (i.e., reinforcement learning) [49,50].”
Laila, K., Jayashree, P. R., & Jainish, G. R. (2025) · Advances in intelligent systems and computing · doi:10.1007/978-981-96-8563-9_16
Navigating Preferences With Artificial Intelligence
omari, S. E., & Idrissi, N. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8011-7.ch007
Kaushik, S., & Verma, R. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s101623722550067x
Metadata
- Authors
- Portugal, I., Alencar, P., & Cowan, D.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Portugal, Ivens
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 97
- Pages
- 205-227
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2263972221
- Cited by
- 743
- References
- 118
- Citations by year
- 2026: 34 · 2025: 81 · 2024: 97 · 2023: 95 · 2022: 106 · 2021: 113 · 2020: 104 · 2019: 69 · 2018: 36
- FWCI
- 18.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Expert Systems with Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UWSpace (University of Waterloo) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- NSERC
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- recommender systems, machine learning algorithms, algorithm selection, big data technologies, performance indicators, research trends, research opportunities
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