Cited by 743Recommender Systems and TechniquesInformation Retrieval and Search BehaviorAdvanced Bandit Algorithms Research

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The use of machine learning algorithms in recommender systems: A systematic review

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Portugal, Ivens, Alencar, Paulo, Cowan, Donald·2018·Expert Systems with Applications

DOI 10.1016/j.eswa.2017.12.020

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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.”
Method
Data clustering
Jain, 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…”
Background
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
Adomavičius, 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].”
Background
Software engineering: A practitioner's approach
(1983) · Advances in Engineering Software (1978) · doi:10.1016/0141-1195(83)90118-3 · Cited by 8,227
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The Hadoop Distributed File System
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“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.”
Method
Bayesian network classifiers
Friedman, 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…”
Background
Supervised 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.”
Background
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115

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Sorted by citations

Items 401–500 of 742

Model Building and Heuristic Evaluation of Various Machine Learning Classifiers
Jahnavi, Y., Balasaraswathi, V. R., & Kumar, P. N. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1431-9_30 · Cited by 4
Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques
Trigunasih, N. M., Narka, I. W., & Saifulloh, M. (2023) · Journal of Degraded and Mining Lands Management · doi:10.15243/jdmlm.2023.111.5073 · Cited by 4
“…popular algorithms, such as Back Propagation (BP), Linear Regression (LR), Random Forest (RF), Naive Bayesian (NB), Rocchio Method (RM), Decision Tree (DF), and Neural Network (NN) (Negnevitsky et al., 2005; Negnevitsky and Pavlovsky, 2005; Portugal et al., 2018; Uddin et al., 2019; Batta, 2020).”
Method
Machine Learning for Managing Modeling Ecosystems: Techniques, Applications, and a Research Vision
Ruscio, D. D., Nguyen, P. T., & Pierantonio, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-36060-2_10 · Cited by 4
The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact
Park, J., & Cordell, R. (2023) · Journal of Information Science · doi:10.1177/01655515231212977 · Cited by 5
“…cars [2] aided by computer vision; (2) researchers are using natural language processing (NLP) to detect hate speech, as a way of efficient moderation of toxic language in online communities [3]; and (3) ML is used to recommend products to improve the user experience of online shopping [4].”
Havayolu bozulma yönetiminde gecikme tahmini için makine öğrenmesi algoritmaları ile sınıflandırma
Yaman, N., & Atmaca, E. (2024) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.1199784 · Cited by 4
Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations
Provezza, L., Bodini, I., Petrogalli, C., Lancini, M., Solazzi, L., & Faccoli, M. (2021) · Metals · doi:10.3390/met11020283 · Cited by 4
“…study aims to quantitatively estimate the damage progress of a specimen during a test with continuous and non-contact measurements, using a non-supervised Metals 2021 , 11 , 283 2 of 18 machine learning technique [10–13], called k-means clustering [14], applied to vibrations and torque datasets.”
Method
HYBRID RECOMMENDATION APPROACH BASED ON A VOTING SYSTEM: EXPERIMENTATION IN AN EDUCATIONAL CONTEXT
Baidada, M., & Poirier, K. M. A. F. (2020) · doi:10.33965/el2020_202007l004 · Cited by 4
“To assess our recommendation system, we used the three indicators commonly used in this area (Isinkaye et al.., 2015; Portugal et al., 2017), namely: - Precision: means the percentage of results which are relevant, it measures the quality of the recommendations (the accuracy).”
Method
Framework of business intelligence systems for infrastructure design and management
Jaffari, R., Memon, M. A., Hafiz, T., & Iftikhar, R. (2017) · International Conference on Latest Trends in Electrical Engineering and Computing Technologies (INTELLECT) · doi:10.1109/intellect.2017.8277619 · Cited by 4
“We aim to apply “supervised machine learning” techniques which use a training set and some predefined classification/correct answers/labels to learn the system and then apply the gained knowledge to some real data [2].”
BackgroundMethod
Local Tours Recommendation Applying Machine Learning in Social Networks
Medina, B., Pineda, A. I. M., Guzmán, G., Jiménez, L. I. G., & Mata, F. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-62554-2_31 · Cited by 4
System Framework for an Intelligent Question Bank and Examination System
(2018) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2018.8.5.734 · Cited by 4
Analysis of the preferences of public transport passengers in the task of building a personalized recommender system
Borodinov, A. A., & Myasnikov, V. (2019) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.18287/1613-0073-2019-2391-198-205 · Cited by 4
Market Basket analysis in Retail
Grau, G. R. (2017) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 4
A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research BeginnersPreprint
Wang, B., Weng, Z., & Wang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08819 · Cited by 4
Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature ReviewPreprint
Ommane, Y., Rhanbouri, M. A. A., CHOUIKH, H., Jbene, M., Chairi, I., Lachgar, M., & BENJELLOUN, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1224662/v2 · Cited by 4
De novo Molecular Design using Deep Learning
Nguyen‐Vo, T. (2024) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.25044362 · Cited by 4
The role of deep learning in drug discovery
Ahmad, A., & Mohamed, A. A. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27574-6.00001-1 · Cited by 3
Precise prediction of visual and non-visual parameters of white light-emitting diodes by back propagation neural networks and principal component analysis
Li, Z., Yang, K., Ye, L., Huang, J., Xia, F., Sun, Z., Chai, Y., Chen, Z., & Guo, Z. (2025) · Measurement · doi:10.1016/j.measurement.2025.116885 · Cited by 3
Energy Consumption Optimization in Mining Operations via Machine Learning Techniques
Surya, S., Srinadh, V., Tripathi, S., Saxena, K., Inumula, K. M., & Quraishi, S. J. (2025) · Journal of Mines Metals and Fuels · doi:10.18311/jmmf/2025/49115 · Cited by 3
Identification of geological conditions based on stacking algorithm during slurry shield tunneling
Zeng, Y., Zhao, W., Yu, Z., Wei, K., & Zhang, X. (2026) · Smart Construction and Sustainable Cities · doi:10.1007/s44268-025-00080-8 · Cited by 3
“[17–20] A growing body of research has applied AI/ML techniques to various aspects of tunnel construction, including set-tlement prediction, parameter optimization, posture control, and geological condition identification.”
Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers
Cruz, C., Van, C. N., & Gautier, L. (2018) · HAL · Cited by 3
Analyzing Public Opinions Regarding Virtual Tourism in the Context of COVID-19: Unidirectional vs. 360-Degree Videos
Hoc, H. T., Šilhavý, P., Dey, S. K., Hoang, S. D., Prokopová, Z., & Šilhavý, R. (2022) · Information · doi:10.3390/info14010011 · Cited by 3
Introduction to Artificial Intelligence & ML
Panat, S., & Kumar, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-22206-1_5 · Cited by 6
Cutting tools to optimize classification parameters of timber species with convolutional neural networks
Centeno, T. B., Ferreira, C. A., Guillen, J. G. I., Echeverry, A. V., Buendia, R. H., Alanya, O. D. V., Moya, S. M., Aliaga, D. C. R., Centeno, W. G., Miranda, B. S. A., et al. (2023) · Revista de Biología Tropical · doi:10.15517/rev.biol.trop..v71i1.51310 · Cited by 3
RRL: Recommendation Reverse Learning
You, X., Xu, J., Zhang, M., Gao, Z., & Yang, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28782 · Cited by 3
GWO-CNN: Trust and Intelligent Recommendation system based model to improve marketing strategies on e-commerce
Sethi, V., Kumar, R., & Mehla, S. (2024) · IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) · doi:10.1109/icwite59797.2024.10503491 · Cited by 3
Anomaly Detection Algorithms in Cybersecurity
Manavadaria, M. S., Srinivas, T. A. S., Ahamed, S. K., Amshavalli, M. M., Nadaf, A. B., & Bhoopathy, V. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7540-2.ch013 · Cited by 3
Development of Multimodal Recommender System for Advancing Design for Safety with Expert Validation
Kumi, L., Jeong, J., Jeong, J., & Mun, H. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-16802 · Cited by 3
Towards Smart Emergency Management: Trends and Challenges of Feature Engineering
Sahoh, B., & Choksuriwong, A. (2018) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2018.8712690 · Cited by 3
“It utilizes 5W features to generate current knowledge, which the authorities will employ to make decisions inside the Application and Decision Support System according to the blueprint studied in [19].”
Method
Machine learning as a tool for choice of enterprise development strategy
Krichevsky, M. L., & Serova, E. G. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202022403006 · Cited by 3
İçerik tabanlı bilimsel yayın öneri sisteminde benzerlik ölçümlerinin incelenmesi
Deniz, E., Öz, V. K., Keser, S. B., Okyay, S., & Kartal, Y. (2021) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.838084 · Cited by 3
Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm
Gómez-Losada, Á., Cortés, G. A., & Duch‐Brown, N. (2022) · Information · doi:10.3390/info13020044 · Cited by 3
An improved network-based recommendation model via inhibiting algorithm bias
Qiu, T., Lu, T., Chen, G., & Zhang, Z. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.127109 · Cited by 3
Recommender systems: a novel approach based on singular value decomposition
Colace, F., Conte, D., Santo, M. D., Lombardi, M., Paternoster, B., Santaniello, D., & Valentino, C. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i6.pp6513-6521 · Cited by 3
“By processing large quantities of data and using increasingly sophisticated algorithms, the ultimate goal is to provide precise suggestions to users [3].”
Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis
Macri, C. Z., Bacchi, S., Teoh, S. C., Lim, W. Y., Lam, L., Patel, S., Slee, M., Casson, R. J., & Chan, W. O. (2023) · Journal of Medical Internet Research · doi:10.2196/42789 · Cited by 3
Prediction of CO2, CH4 diffusion fluxes at the water-air interface and analysis on its influencing factors using machine learning algorithms in the Three Gorges Reservoir
Changyue, O., Qin, Y., Liu, Z., & Liang, Y. (2023) · Journal of Lake Sciences · doi:10.18307/2023.0206 · Cited by 4
An Adaptive Approach for Fault Localization using R-CNN
Ghosh, D., Singh, J. P., & Singh, J. (2022) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic55218.2022.10088417 · Cited by 3
“Machine learning algorithms are widely employed in many fields nowadays such as image processing, smart phone technologies, healthcare, E-commerce websites, etc. [8].”
Background
HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education
Marina., B., & Senthilrajan, A. (2023) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2023.13.4.1855 · Cited by 3
“The study determined that Decision Tree and Bayesian algorithms are extensively employed in RS and it offered new prospects for researches [17].”
Background
Federated Recognition Architecture Based on Voting and Feedback Mechanisms for Accuracy Object Classification in Distributed Edge Intelligence Environment
Tuan, N. A., Xu, R., Moe, S. J. S., & Kim, D. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3285618 · Cited by 3
“The recommendation systems employed by companies like Netflix and Amazon are prime examples of online learning [41], [42].”
Method
Recommendation of UML Model Conflicts: Unveiling the Biometric Lens for Conflict Resolution
Dalcin, G. K., Bolzan, W., Lazzari, L., & Farias, K. (2023) · Brazilian Symposium on Software Engineering · doi:10.1145/3613372.3613378 · Cited by 3
“Machine learning, an Artificial Intelligence (AI) field, involves using algorithms to predict outcomes based on available data [24].”
Background
A Survey on Recommendation Methods Based on Social Relationships
Chen, R., Pang, K., Huang, M., Liang, H., Zhang, S., Zhang, L., Li, P., Xia, Z., Zhang, J., & Kong, X. (2023) · Electronics · doi:10.3390/electronics12224564 · Cited by 3
A Comprehensive Survey on Machine Learning Approaches in Course Recommendation System
Khan, S. A., Ali, S., Deorankar, A., Dhomble, R., & Shah, S. A. (2024) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs61402.2024.10482042 · Cited by 3
“The present article discusses how computers utilize ML and algorithms to make recommendations to individuals, such as which films to see or which products to buy (review [13]).”
Background
Modeling higher-order social influence using multi-head graph attention autoencoder
Meydani, E., Düsing, C., & Trier, M. (2024) · Information Systems · doi:10.1016/j.is.2024.102474 · Cited by 3
Knowledge management for product innovation design based on dual-layer concept network
Liu, Q., Zhang, J., Fang, P., Shi, Q., Liu, X., & Li, D. (2025) · International Journal of Modern Physics C · doi:10.1142/s0129183124420105 · Cited by 3
Enhancing Crowdfunding Success With Machine Learning and Visual Analytics: Insights From Chinese Platforms
Wang, Y., & Ibrahim, O. (2025) · IEEE Access · doi:10.1109/access.2025.3546265 · Cited by 3
Symptom Recommendation using Collaborative Filtering and Disease Prediction using Support Vector Machine
Kamath, A., Parab, A., & Kerkar, N. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018916977 · Cited by 3
Integrating Sentiment Analysis and User Descriptors with Ratings in Sightseer Recommender System
Chaudhari, V. A., Kshirsagar, V., & Kshirsagar, M. (2018) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8494035 · Cited by 3
Comparative Study of Machine Learning Algorithms for Recommendation System
Singh, S., & Srivastva, K. (2020) · Journal of University of Shanghai for Science and Technology · doi:10.51201/jusst12495 · Cited by 3
Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System
Sahoo, A. K., Raj, S., Pradhan, C., Mishra, B. S. P., Barik, R. K., & Vidyarthi, A. (2021) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.2022010115 · Cited by 3
Structured Analysis on Movie Recommendation System using Machine Learning
Medikonduru, M. S., Bypureddy, P., Kancharla, V. P., Kamarajugadda, V. N. H. Y. P., & Attluri, L. C. (2022) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs53718.2022.9788351 · Cited by 3
Machine Learning the TV Consumption: A Basis for a Recommendation System
Cardoso, B., & Abreu, J. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-90170-1_5 · Cited by 3
Modeling Decision-Making with Intelligent Agents to Aid Rural Commuters in Developing Nations
Gerpe, P. J., & Markopoulos, E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-20454-9_52 · Cited by 3
Top-N Hashtag Prediction via Coupling Social Influence and Homophily
Wang, C., Sun, Z., Zhao, Y., Chi, C., van den Heuvel, W., Lam, K., & Stantić, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35231-8_25 · Cited by 3
Survey on Machine Learning in 5G
Rohini, M., Selvakumar, N., Suganya, G., & Shanthi, D. (2020) · International Journal of Engineering Research and · doi:10.17577/ijertv9is010326 · Cited by 3
A clinical coding recommender system
Suleiman, M., Demirhan, H., Boyd, L., Girosi, F., & Aksakallı, V. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106455 · Cited by 3
A systematic mapping study on agent mining
Strugeon, E. G.-L., de Oliveira, K. M., Thilliez, M., & Petit, D. (2021) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2020.1864784 · Cited by 3
“Some of the studies included the use of intelligent agent systems (Edwards et al., 2015; Liao et al., 2012; Portugal et al., 2018).”
Background
Beyond Deep Event Prediction: Deep Event Understanding Based on Explainable Artificial Intelligence
Sahoh, B., & Choksuriwong, A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-64949-4_4 · Cited by 3
Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models
Saito, F., Yamashita, H., Sasaki, H., & Goto, M. (2021) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia52852.2021.9626037 · Cited by 3
Analysis of Factors Affecting the Success of Sustainable Development Projects with the Help of Machine Learning Tools
Chen, Z.-J., Hsieh, T.-S., & Davoudi, S. M. M. (2022) · Discrete Dynamics in Nature and Society · doi:10.1155/2022/1956879 · Cited by 3
A naturopathy knowledge graph and recommendation system to boost the immune system
Gyrard, A., & Boudaoud, K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-391773-5.00014-5 · Cited by 3
Recommendation Systems and the Use of Machine Learning Methods
Peuker, A., & Barton, T. (2023) · Springer eBooks · doi:10.1007/978-3-658-38798-3_6 · Cited by 3
Product Recommendation System using Deep Learning based Recurrent Neural Network
Shanthi, S. A., Nirmaladevi, K., Mathiarasi, N., Tharani, A. K., Monisha, A., Kuppuchamy, R., & Krishnamoorthy, N. V. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128563 · Cited by 3
“Portugal I, Alencar P, Cowan D[8] published a paper describing the application of machine learning algorithms in recommender systems is examined in a thorough review of the literature, which also identifies new areas for further research.”
Background
Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research SurveyPreprint
Rama, K., Kumar, P., & Bhasker, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08674 · Cited by 3
“…rating for the item or as a list of recommendations or as a binary score indicating preference for the item” (Handbook, RICCI, & ROKACH, 2011), (Portugal, Alencar, & Cowan, 2017) (Cheng et al., 2016) (S. Zhang & Yao, 2017) Advances in graphical processing unit hardware and decreasing costs…”
Background
Incorporating Artificial Intelligence into Design Criteria Considerations
Drzyzga, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60611-3_10 · Cited by 2
Image recommendation for social media marketing in maternity and baby care product industry – a machine learning approach
Wang, K., & Wang, J.-A. (2024) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-04-2024-0463 · Cited by 2
Citizen science to approach machine learning to society: Detecting loneliness in older adults
Castillo-Hornero, A., Belmonte, Ó., Gascó-Compte, A., Miedes, A. C., López, A., & Afxentiou, A. (2024) · Digital Health · doi:10.1177/20552076241292809 · Cited by 2
“10 In this case, the data that the ML method learns from is a set of previously classi ﬁ ed movie reviews, the data the ML method uses to classify the new review is its text, and the output is the group the review belongs to (positive, neutral, or negative).”
Method
Collaborative Fair-is-Better Filtering for Implicit Feedback
Dong, H. V., Nguyen, H., Nguyen, H. D., & Le, D.-T. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.599 · Cited by 2
Federated Learning for Recommender Systems: Advances and Perspectives
Perifanis, V., Pavlidis, N., Sendros, A., & Efraimidis, P. S. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-78841-3_5 · Cited by 3
A first principles approach to trust-based recommendation systems in social networks
Stefanopoulos, P., Chatterjee, S., & Zehmakan, A. N. (2025) · Online Social Networks and Media · doi:10.1016/j.osnem.2025.100315 · Cited by 2
“Bayesian [13, 15] and decision tree [34, 49] approaches are mostly used due to their simplicity [43].”
Recommender Systems in Health Professions Education: Protocol for a Scoping Review
Healy, P. M., O’Tuathaigh, C. M. P., Heavin, C., McCarthy, N., & Latifi, S. (2025) · JMIR Research Protocols · doi:10.2196/69979 · Cited by 3
Retrieval Augmented Generation Model for Paper Recommendation System
Yadav, N., & Gopinathan, D. (2025) · SN Computer Science · doi:10.1007/s42979-025-04095-x · Cited by 2
“An example is Pazzani’s [ 73 ] hybrid system, which surpassed single-method systems by blending content-based and collaborative filtering for website recommendations [ 74 ].”
Deep Learning and Ensemble Methods for Robust Financial Email Phishing Detection: A Performance Benchmark
Mansoori, F., Chauhan, K., & Kumar, S. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070780 · Cited by 2
Agentic Recommender Systems: A Systematic Literature Review
Portugal, I. D. S., Alencar, P., & Cowan, D. D. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3657900 · Cited by 3
“More traditional and alternative metrics for recommender systems are found in [121]. a) Software engineering non-functional requirements are metrics: The result in Table XIX indicates efforts to evaluate agentic RSs beyond accuracy or precision.”
Self-citation
Tutorial on Benchmarking Big Data Analytics Systems
Ivanov, T., & Singhal, R. (2020) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3375555.3383121 · Cited by 2
“The use of big data technologies for building machine-learning [39] and deep-learning [50] pipelines, and models has introduced more complexity in choosing the right model building framework, libraries and hardware architecture.”
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Wong, A.-N., & Raheem, M. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1712/1/012043 · Cited by 2
“Out of 26 research articles on recommender models that were reviewed by reference [22], the Bayesian and decision tree are the top two machine learning algorithms used probably due to their simplicity as presented in Table 2.”
Background
Empfehlungssysteme und der Einsatz maschineller Lernverfahren
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Vaishnavi, M., & Vemuru, S. (2023) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc56507.2023.10084109 · Cited by 2
“Mathematical definition of Federated learning [31] where each client/participant in federated learning is represented by 𝐶 𝑖 , where 𝑖 ∈ [1, 𝑛] Differential privacy is a mathematical definition of privacy that provides a rigorous standard for protecting the privacy of individuals in a dataset.”
BackgroundMethod
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Mohebbi, N., Alavi, M., Kargari, M., Mirbehbahani, S. H., & Kharazmi, A. B. (2023) · International Computer Conference, Computer Society of Iran (CSICC) · doi:10.1109/csicc58665.2023.10105347 · Cited by 2
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“ML is a field of AI that highlights the process of building algorithms and models that allow computers to learn and predict on unseen datasets without being explicitly programmed [7].”
Background
Garbage Image Classification Using Improved Inception-V3 Neural Network
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“From these foundational works, using machine learning for recommendation has become a core strategy on information-based companies (Portugal et al. 2018) such as Google (Liu et al. 2010), X (formerly known as Twitter) (Ahmed et al. 2013), LinkedIn (Rodriguez et al. 2012) and Netflix (Steck 2013).”
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“The application incorporates machine learning algorithms to enhance user experience by predicting the best coupons based on user preferences, usage history, and expiration timelines [8].”
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“Recent work integrates predictive modeling with recommendation systems [3, 19].”
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“Some examples of possible resolutions are Support Vector Machines (SVM) [3], Artificial Neural Networks - Perceptron Multilayer (MLP) [4] and the k-nearest neighbors [5].”
Background
Design and Control of Multifunctional, Multiarticulate Prosthetic Hand
Ayachi, M., & Seddik, H. (2021) · Advanced information and knowledge processing · doi:10.1007/978-3-030-14939-0_1 · Cited by 2
Artificial Intelligence and Financial Markets in Smart Cities
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Recommender system: An analytical report on decision making for large scale online social networks
Goyal, R., & Goyal, S. J. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.06.311 · Cited by 2
Literature Review on Information Filtering Methods in Recommendation Systems
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“The user is then given recommendations based on the results of the search[3].”
Method
A Comparison of Performance of Rough Set Theory with Machine Learning Techniques in Detecting Phishing Attack
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HYBRID EDUCATIONAL RECOMMENDATION SYSTEM: EXPERIMENTATION WITH A VOTING-BASED EVALUATION MODE
(2020) · IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET · doi:10.33965/ijwi_202018107 · Cited by 2
“To assess our recommendation system, we used the three indicators commonly used in this area (Isinkaye et al.., 2015; Portugal et al., 2017), namely: - Precision: means the percentage of results which are relevant, it measures the quality of the recommendations (the accuracy).”
Method

Authors: Portugal, I., Alencar, P., & Cowan, D.
Affiliations: University of Waterloo (CA)
Corresponding author: Portugal, Ivens
ORCID: Portugal, Ivens
Alencar, Paulo
Cowan, Donald
Venue: Expert Systems with Applications
Publisher: Elsevier BV
Published: 2018-05
Volume: 97
Pages: 205-227
ISSN: 0957-4174 (ISSN-L)
DOI: 10.1016/j.eswa.2017.12.020
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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The use of machine learning algorithms in recommender systems: A systematic review

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