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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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Personalized intelligent detection and recommendation algorithm system based on big data
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BackgroundA Recommender Systems Based on Similarity Networks: MovieLens Case Study
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“The major recommendation approaches include: collaborative filtering (CF) [3], contentbased (CB) [4], knowledge-based [5], hybrid methods [6], friendship strength-based recommender [7], machine learning algorithms [8] such as Bayesian and decision tree and Singular value decomposition (SVD) methods [9].”
BackgroundTags-Aware Recommender Systems: A Systematic Review
Shoja, B. M., & Tabrizi, N. (2019) · IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science (BCD) · doi:10.1109/bcd.2019.8884850 · Cited by 8
“criteria to analyze retrieved publications in order to eliminate irrelevant publications according to [13].”
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Wang, Y., Mei, S., & Zhong, W. (2022) · Managerial and Decision Economics · doi:10.1002/mde.3513 · Cited by 8
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A Systematic Study on a Customer’s Next-Items Recommendation Techniques
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S-RAP: relevance-aware QoS prediction in web-services and user contexts
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“[1] Recommender systems that use a collaborative filtering approach consider user data to predict ratings and make appropriate recommendations.”
BackgroundScientific Paper Recommendation System
Singh, R., Gaonkar, G., Bandre, V., Sarang, N., & Deshpande, S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126196 · Cited by 8
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BackgroundA Survey and Classification on Recommendation Systems
Sharma, M., Mittal, R., Bharati, A., Saxena, D., & Singh, A. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-3481-2_44 · Cited by 8
Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems
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“The process of ESR ESR: Equity subscription rate The rapid development of artificial intelligence (AI) and computing, many industries have implemented AI models for improving the efficiency of solving problems [24, 37, 1, 36, 17].”
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Amer, A. A., & Nguyen, L. (2021) · arXiv (Cornell University) · doi:10.13140/rg.2.2.15294.41280 · Cited by 8
Dawn of Big Data with Hadoop and Machine Learning
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In-depth Analysis of Artificial Intelligence for Climate Change MitigationPreprint
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Benchmarking quantum versions of the kNN algorithm with a metric based on amplitude-encoded features
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“Word embedding models are trained on vast amounts of text using neural networks or machine learning [35-39].”
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“The papers [30-32] analyse different ML algorithms such as multinominal NB, RF, Bernoulli Naïve Bayes, Convolutional Neural Network, Decision Tree (DT), and Long Short-Term Memory RNN in the field of recommendation systems.”
MethodRevolutionizing Digital Marketing Using Machine Learning
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Deep Learning-based Educational User Profile and User Rating Recommendation System for E-Learning
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Machine Learning in Gaze-Based Interaction: A Survey of Eye Movements Events Detection
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Reviewing review platforms: a privacy perspective
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“Another branch of research applies machine learning techniques to build recommender systems [22] for users based on their exposed interests [13, 25].”
BackgroundDrug Recommender Systems: A Review of State-of-the-Art Algorithms
Omodunbi, T. O., Alilu, G. E., & Ikono, R. N. (2022) · Information Technology for Education and Development (ITED) · doi:10.1109/ited56637.2022.10051591 · Cited by 7
“These are the collaborative filtering (CF), the content-Based (CB) and the Hybrid approach, with each having its advantages and limitations [4], [8], [9], [10], [17], [21], [23].”
MethodRecommender System: Collaborative Filtering of e-Learning Resources.
Mbaye, B. (2018) · International Association for Development of the Information Society · Cited by 7
A Study on Performance Improvement of Heart Disease Prediction by Attribute Selection Methods
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“Researchers face difficulties in keeping up with the latest trends and understanding which algorithms are most effective for specific types of RSs [10].”
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Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches
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A survey on recommendation in process mining
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Taming Multi-Output Recommenders for Software Engineering
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“Recent years have witnessed the use of machine learning and deep learning in many recommender systems, making them more accurate and efficient [28].”
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Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
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“presented at the right moment and need to be precise, meaningful, effective, comprehensible and verifiable at any given time [9], [10].”
BackgroundHuman Strategic Steering Improves Performance of Interactive Optimization
Colella, F., Daee, P., Jokinen, J., Oulasvirta, A., & Samuel Kaski, A. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3394883 · Cited by 6
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BackgroundAnalysis of Classification Algorithm for Wisconsin Diagnosis Breast Cancer Data Study
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“With the Gradient Boosted Decision Tree method, it can be minimized until it reaches what it wants[22][23][24].”
MethodA Time‐Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users
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Accuracy‐Assured Privacy‐Preserving Recommender System Using Hybrid‐Based Deep Learning Method
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Recommendation System For Anime Using Machine Learning AlgorithmsPreprint
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“We consider that the SEM could be integrated with some recommender system, implemented with machine learning techniques [27], to learn the relevance of added signifiers and adapt signifier exposure accordingly.”
An Explainable By-Design Framework for Transparent User-Centric AI Energy Services
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P2Net: A Two-Stage Personalized Pedestrian Dead Reckoning Based on Neural Networks
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“Machine learning approaches have provided scholars with rich avenues for the evaluation and design of recommender systems [43].”
Understanding Decentralized Social Feed Curation on Mastodon
Liu, Y., Song, E., Zhang, O. X., Merriman, J., Zhang, L., & Monroy‐Hernández, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757688 · Cited by 13
“Additionally, Mastodon operates on the ActivityPub protocol [80], which differs in decentralized architecture and information flow from other protocols such as ATProtocol [72] and Farcaster [25].”
HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education
Marina., B., & Senthilrajan, A. (2023) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2023.02.02 · Cited by 5
High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs
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“Traditional statistical and testing approaches have been widely used to scale up HIV testing interventions worldwide; while they are effective, they can be subject to certain limitations, such as accuracy, precision, and privacy issues [14].”
Murugesan, S., & Muthurajkumar, S. (2022) · Automatika · doi:10.1080/00051144.2022.2140389 · Cited by 5
“Portugal et al. [20] analyzed the use of algorithms based on machine learning in the recommender sys-tem and found the chances of study for research of software engineering.”
MethodBECOME: A Modular Recommender System for Coaching and Promoting Empowerment in Healthcare
Orte, S., Migliorelli, C., Sistach-Bosch, L., Subías-Beltrán, P., Fritzsche, P. C., Galofré, M., Gómez-Martínez, M., Miralles, F., Marí, D., & Ribas, V. J. (2023) · Artificial intelligence · doi:10.5772/intechopen.109710 · Cited by 7
Recommendation System and Its Techniques in Machine Learning: A Survey
Banwala, N., Singh, G., Singh, J., Goswami, V. S., & Bagnia, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9858-4_17 · Cited by 5
Food Recommendation System Based on Data Clustering Techniques and User Nutrition Records
Al-Chalabi, H., & Jasim, M. N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35442-7_8 · Cited by 5
Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review
Ommane, Y., Rhanbouri, M. A. A., CHOUIKH, H., Jbene, M., Chairi, I., Lachgar, M., & Benjelloun, S. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-37454-8_2 · Cited by 5
Whale optimization algorithm and its application in machine learning
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“In contrast, traditional ML focuses on pattern recognition and data mining from large datasets to achieve optimal predictive performance, making revolutionary advancements in fi elds like image and voice recognition (Haeb-Umbach et al., 2019; Portugal et al., 2018).”
Garment Fashion Recommendation System for Customized Garment
Sharma, S., Koehl, L., Bruniaux, P., & Zeng, X. (2019) · International Conference on Industrial Engineering and Systems Management · doi:10.1109/iesm45758.2019.8948164 · Cited by 6
Cserdi, Z. H., Fehér, P., Gáti, M., Rab, Á. S., & Varga, K. (2022) · Society and Economy · doi:10.1556/204.2022.00002 · Cited by 8
Using Deep Learning for Positive Reviews Prediction in Explainable Recommendation Systems
Zarzour, H., Alsmirat, M. A., & Jararweh, Y. (2022) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics55353.2022.9811151 · Cited by 5
“…with collaborative methods based on users data such as [1]; recommender systems with content-based filtering methods using items data; and recommender systems combining the collaborative and the content-based filtering methods into a new category of methods called hybrid filtering [2], [3] .”
MethodLe, J. P., Shashikumar, S. P., Malhotra, A., Nemati, S., & Wardi, G. (2023) · Critical Care Clinics · doi:10.1016/j.ccc.2023.02.003 · Cited by 5
Prediction and Evaluation of Cancer Using Machine Learning Techniques
Jahnavi, Y., Kumar, P. N., Anusha, P., & Prasad, M. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1431-9_31 · Cited by 5
Testing Environmental Models supported by Machine Learning
Júnior, V. A. D. S., da Silva, L. A. R., & Neto, P. R. D. A. (2018) · doi:10.1145/3266003.3266004 · Cited by 5
Fuzzy Rough Set based Social IoT Recommender System
Dua, A., Sharma, P., Kumar, K., Kumar, N., & Singh, U. N. (2019) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants47819.2019.9118090 · Cited by 5
“Recommendation systems are combination of techniques such as data mining, information filtering and search engines, and they predict item rating based on user’s preferences in the past [5].”
BackgroundFigueroa, C., Vagliano, I., Rocha, O. R., Torchiano, M., Zucker, C. F., Corrales, J. C., & Morisio, M. (2018) · Advances in web technologies and engineering book series · doi:10.4018/978-1-5225-7186-5.ch002 · Cited by 5
“Algorithms in this type are classified in Supervised learning and unsupervised learning (Portugal, Alencar, & Cowan, 2018): • Supervised learning.”
MethodAt Your Service: Coffee Beans Recommendation From a Robot AssistantPreprint
de Berardinis, J., Pizzuto, G., Lanza, F., Chella, A., Meira, J., & Cangelosi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.13585 · Cited by 5
“ge to address would be the deployment of an intelligent coffee recommender system1 on a humanoid robot in a coffee shop, as illustrated in Figure 1. Recommendation systems are increasingly being used [6]. In the hospitality industry, recommendation systems are being used to recommend healthy food options [7] or the pe…”
BackgroundDeep Reinforcement Learning-Based Product Recommender for Online AdvertisingPreprint
Esfahaani, M. V., Xue, Y., & Setoodeh, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00333 · Cited by 5
“These algorithms are divided into three general categories: supervised, unsupervised, and reinforcement learning [3].”
BackgroundMachine Learning based Recommender Systems for Crop Selection: A Systematic Literature ReviewPreprint
Ommane, Y., Rhanbouri, M. A. A., CHOUIKH, H., Jbene, M., & Chairi, I. (2022) · Research Square · doi:10.21203/rs.3.rs-1224662/v1 · Cited by 5
Detecting Fraudulent Transactions Using a Machine Learning Algorithm
Belyakov, S. L., Karpov, S. М., & Zalilov, E. F. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-1480-4_4 · Cited by 4
Subramanian, K., Sathiyagnanam, A. P., & Dillikannan, D. (2024) · Chemical Industry and Chemical Engineering Quarterly · doi:10.2298/ciceq240303025s · Cited by 4
Fan, C., Wang, W., Cui, T., Liu, Y., & Qiao, M. (2024) · Processes · doi:10.3390/pr12112362 · Cited by 4
A Two-Stage Deep Learning Approach for Optimizing Fashion Product Recommendations
Buradagunta, S., & Balakrishna, S. (2025) · SN Computer Science · doi:10.1007/s42979-025-03909-2 · Cited by 4
“Initially Initially, these systems used machine learning models [18, 19].”
Movie Recommendation System Approaches
Praveen, P., Parmar, S., & Goud, P. A. (2020) · International Journal of Engineering Research and · doi:10.17577/ijertv9is040710 · Cited by 4
Crowdsourced Social Media Reaction Analysis for Recommendation
Verma, J. P. V., Tanwar, S., Garg, S., & Rathod, A. D. (2021) · International Journal of Knowledge and Systems Science · doi:10.4018/ijkss.2021010101 · Cited by 4
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Wegmeth, L., Vente, T., & Purucker, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56027-9_9 · Cited by 4
Device Selection Methods in Federated Learning: A Survey
Mattoo, A., Jain, N., & Gandhi, C. (2024) · SN Computer Science · doi:10.1007/s42979-024-03137-0 · Cited by 5
“Machine learning has a massive and wide scope of applications like natural language processing [3], image recognition [4], recommender systems [5], fraud detection [6], and predictive analytics [7].”
A fact based analysis of decision trees for improving reliability in cloud computing
Shahid, M. A., Alam, M. M., & Su’ud, M. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311089 · Cited by 4
K-means clustering approach for damage evolution monitoring in RCF tests
Chiesa, M. B., Bodini, I., Petrogalli, C., Provezza, L., Faccoli, M., Mazzù, A., Solazzi, L., Sansoni, G., & Lancini, M. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1065/10/102018 · Cited by 4
Borodinov, A. A., & Myasnikov, V. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39575-9_34 · Cited by 4
Advances in the application of Ontologies in the area of Digital Forensic Electronic Mail
de Gallo, B. P., Vegetti, M., & Leone, H. (2019) · IEEE Latin America Transactions · doi:10.1109/tla.2019.8986448 · Cited by 4
“La metodología propuesta en [10] formula el proceso de revisión en fases claramente identificadas permitiendo la generación de un marco de trabajo ordenado que ayuda en todo el proceso de revisión, orientando al investigador en cada fase, evitando desvíos que insumen tiempo y esfuerzo.”
BackgroundInfluentialAn Opinion Facilitator for Online News Media
Willaert, T., Eecke, P. V., Soest, J. V., & Beuls, K. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.695667 · Cited by 4
“, 2021), and the technological development of recommender systems for the news industry has grown into an active area of research (Portugal et al., 2018; Da’u and Salim, 2020; Fengh et al., 2020).”
BackgroundPlate Number Recognition Using Segmented Method With Artificial Neural Network
Bal, C., Yunusa, A. S., Abdu, A. M., Abdullahi, A. A., Musa, A. L., & Sani, A. M. (2021) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec54230.2021.9672356 · Cited by 4
Chen, R., Zhang, J., Zhang, Z., Chang, Y., Gao, J., Li, P., & Liang, H. (2022) · Soft Computing · doi:10.1007/s00500-022-07440-x · Cited by 6
“In (Portugal et al. 2018; Guo et al. 2015), user interest preference models were built from the perspectives of users’ trustees and trusters.”
BackgroundMethodInfluentialSafemedchain — drug counterfeit prevention and recommendation using blockchain and machine learning
Gopikarani, N., Gayathri, B., Praja, S. S., & Sridharan, S. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-220636 · Cited by 4
“[ 27] and [28] lists the different recommendation systems in the healthcare sector, the approaches and their shortcomings.”
Science by YouTube: an Analysis of YouTube’s Recommendations on the Climate Change Issue
Dündar, P., & Ranaivoson, H. R. (2022) · Observatorio (OBS*) · doi:10.15847/obsobs16320222061 · Cited by 4
Artificial Intelligence in Human Resource Management: Personnel Marketing and Recruiting
Lüersmann, A. (2023) · Studies in systems, decision and control · doi:10.1007/978-3-031-27506-7_23 · Cited by 5
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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