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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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“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].”
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BackgroundSupervised Machine Learning: A Review of Classification Techniques
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“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
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Pang, Y., Jiang, P., & Yang, R. (2021) · Journal of Applied Physics · doi:10.1063/5.0057796 · Cited by 39
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“There are three most widely used machine learning models [15] they are supervised learning, unsupervised learning, and reinforcement learning, among which a suitable model can be chosen.”
BackgroundMethodInfluentialPre-processing approaches for collaborative filtering based on hierarchical clustering
Neto, F. S. D. A., da Costa, A. F., Manzato, M. G., & Campello, R. J. G. B. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.021 · Cited by 36
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MethodConvolutional neural networks for enhanced classification mechanisms of metamodels
Nguyen, P. T., Ruscio, D. D., Pierantonio, A., Rocco, J. D., & Iovino, L. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2020.110860 · Cited by 36
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“According to Portugal, Alencar, & Cowan [45], machine learning is an algorithm that “ uses computers to simulate human learning and allows computers to identify and acquire knowledge from the real world, and improve the performance of some tasks based on this new knowledge.”
BackgroundRetractedA social hybrid recommendation system using LSTM and CNN
Daneshvar, H., & Ravanmehr, R. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7015 · Cited by 37
Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge
Strieth‐Kalthoff, F., Szymkuć, S., Molga, K., Aspuru‐Guzik, A., Glorius, F., & Grzybowski, B. A. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c00338 · Cited by 43
Intelligent Software Tools for Recruiting
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“(Powell & Snellman, 2004).”
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“The unsupervised algorithm is searching for previously unknown relationships or patterns between the input data [5], [6], [7].”
BackgroundMethodInfluentialEnvironmental Risk Assessment and Management in Industry 4.0: A Review of Technologies and Trends
Lemos, J., Gaspar, P. D., & Lima, T. M. (2022) · Machines · doi:10.3390/machines10080702 · Cited by 39
Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
Cordeiro-Costas, M., Villanueva, D., Eguía, P., & Granada, E. (2022) · Applied Sciences · doi:10.3390/app12178769 · Cited by 37
“Corresponding to the most employed methodologies to Machine Learning [18] and Deep Learning [19].”
Fusion of edge detection and graph neural networks to classifying electrocardiogram signals
Duong, L. T., Thu, D. T. H., Chu, C. Q., & Nguyen, P. T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120107 · Cited by 34
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Artificial intelligence for solid tumour diagnosis in digital pathology
Klein, C., Zeng, Q., Arbaretaz, F., Devêvre, E., Caldéraro, J., Loménie, N., & Maiuri, M. C. (2021) · British Journal of Pharmacology · doi:10.1111/bph.15633 · Cited by 36
“Machine learning has been around for many years even in medical imaging, but recently it has become the core module for a majority of computer vision (Shekhar et al., 2020), natural language processing (Cambria & White, 2014; Khan & Al-Habsi, 2020) or recommendation systems (Portugal et al., 2018).”
BackgroundDeep neural network approach for a serendipity-oriented recommendation system
Ziarani, R. J., & Ravanmehr, R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115660 · Cited by 32
Prediction of learners’ dropout in E-learning based on the unusual behaviors
Zhou, Y., Zhao, J., & Zhang, J. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1857788 · Cited by 31
“Driven by data mining and big data technology, recommender systems and learning assessments are an important research direction in this fi eld (Portugal et al., 2018; Shute & Rahimi, 2017).”
MethodBlockchain-Secured Recommender System for Special Need Patients Using Deep Learning
Mantey, E. A., Zhou, C., Anajemba, J. H., Okpalaoguchi, I. M., & Chiadika, O. D.-M. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.737269 · Cited by 34
“The study conducted by the authors of (16) presented an overview of the literature that examines how machine learning algorithms are used in recommender systems and also pinpoints opportunities for research in the software engineering field.”
BackgroundA content-based recommendation approach based on singular value decomposition
Colace, F., Conte, D., Santo, M. D., Lombardi, M., Santaniello, D., & Valentino, C. (2022) · Connection Science · doi:10.1080/09540091.2022.2106943 · Cited by 32
“The central aimofRSs consists of providingproper support tousers (Portugal et al., 2018).”
BackgroundComparative Study of Filtering Methods for Scientific Research Article Recommendations
Alaoui, D. E., Riffi, J., Sabri, M. A., Aghoutane, B., Yahyaouy, A., & Tairi, H. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8120190 · Cited by 30
Design and Implementation of a Computer Aided Intelligent Examination System
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Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree
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Machine Learning Approaches for Quality Assessment of Protein Structures
Chen, J., & Siu, S. W. I. (2020) · Biomolecules · doi:10.3390/biom10040626 · Cited by 37
“ML approaches can be broadly classified into four types [36]: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.”
BackgroundSVD++ Recommendation Algorithm Based on Backtracking
Wang, S., Sun, G., & Li, Y. (2020) · Information · doi:10.3390/info11070369 · Cited by 30
Predicting Perovskite Performance with Multiple Machine-Learning Algorithms
Li, R., Deng, Q., Tian, D., Zhu, D., & Lin, B. (2021) · Crystals · doi:10.3390/cryst11070818 · Cited by 31
Hirooka, K., Hasan, M. A. M., Shin, J., & Srizon, A. Y. (2022) · IEEE Access · doi:10.1109/access.2022.3171263 · Cited by 32
“Furthermore, the usage of shallow features results in poor performance utilizing unsupervised learning [36], [37].”
BackgroundA multi-objective artificial bee colony approach for profit-aware recommender systems
Concha-Carrasco, J. A., Vega-Rodríguez, M. A., & Pérez, C. J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.050 · Cited by 30
Machine learning based software fault prediction utilizing source code metrics
Bhandari, G. P., & Gupta, R. (2018) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/cccs.2018.8586805 · Cited by 29
“Basically, learning is the process of knowledge acquisition as human naturally [2].”
BackgroundTowards Deep Object Detection Techniques for Phoneme Recognition
Algabri, M., Mathkour, H. I., Bencherif, M. A., Alsulaiman, M., & Mekhtiche, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.2980452 · Cited by 29
“The AP is a universal metric that can be used to evaluate the performances of different systems, such as information retrieval systems [70], recommender systems [71], and object detection systems [72].”
BackgroundRafi, T. H., Shubair, R. M., Farhan, F., Hoque, M. Z., & Quayyum, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3108892 · Cited by 29
“Currently, it has a large scale applications in online marketing, social sites etc [60].”
BackgroundHasan, M., & Roy, F. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3030039 · Cited by 28
“As RS is a multifaceted sphere, it is researched in the field of statistics [13], calculative trust with or without social network association [14–17], machine learning [18], agent-based artificial networks [19], human–computer interaction [20], and more.”
BackgroundFellowMe Cache: Fog Computing approach to enhance (QoE) in Internet of Vehicles
Abar, T., Rachedi, A., Letaïfa, A. B., Fabian, P., & Asmi, S. E. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.06.026 · Cited by 28
Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN
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Bubble Trouble: Strategies Against Filter Bubbles in Online Social Networks
Burbach, L., Halbach, P., Ziefle, M., & Valdez, A. S. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22219-2_33 · Cited by 28
“Besides these, there are many combinations of techniques used as well [46].”
MethodSu, Z., Lin, Z., Ai, J., & Li, H. (2021) · IEEE Access · doi:10.1109/access.2021.3060016 · Cited by 28
“Collaborative filtering (CF) recommendation models [8] Hybrid recommendation models [9], [10] CF models further include subcategories such as memory-based and model-based methods.”
BackgroundJob recommender systems: a systematic literature review, applications, open issues, and challenges
Ertuğrul, D. Ç., & Bitirim, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01173-y · Cited by 42
Bibliometric monitoring of research performance in ICT-based disaster management literature
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“The co-occurrence network of the ) to infer the situation, using machine learning advanced algorithms (Portugal et al. 2018), computer simulations (Liu et al. 2017), and decision support systems (Zharikova and Sherstjuk 2017).”
MethodMarketing and Financial Services in the Age of Artificial Intelligence
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A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy
Mekouar, L., Iraqi, Y., Damaj, I., & Naous, T. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.01.020 · Cited by 27
Mantey, E. A., Zhou, C., Srividhya, S. R., Jain, S., & Sundaravadivazhagan, B. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.905265 · Cited by 26
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“This technique simulates human behaviors to identify and obtain information and knowledge using computers based on original knowledge and information, to upgrade and improve some abilities of businesses (Portugal et al. 2018).”
MethodDrissi, S., Chefrour, A., Boussaha, K., & Zarzour, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12477-6 · Cited by 32
“…with personalized recommendations by predicting the preference (often expressed in a rating) that the users would give to an item, and typically applying methodologies and techniques from the field of artificial intelligence, such as Machine Learning (Portugal et al., 2018; Khanal et al., 2020).”
Revolutionising Retail Banking with AI and Virtual Technologies AI-Driven Financial Training
Kotte, K. R., Attaluri, V., & Selvakumar, P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9944-6.ch020 · Cited by 25
On Explainable Fuzzy Recommenders and their Performance Evaluation
Rutkowski, T., Łapa, K., & Nielek, R. (2019) · International Journal of Applied Mathematics and Computer Science · doi:10.2478/amcs-2019-0044 · Cited by 25
“For an excellent survey, a reader is referred to the work of Portugal et al. (2018).”
Backgroundseq2vec: Analyzing sequential data using multi-rank embedding vectors
Kim, H. J., Hong, S. E., & Jin, K. (2020) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2020.101003 · Cited by 26
Qi, W., & Li, D. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421590084 · Cited by 25
Niu, B., Liang, R., Zhang, S., Zhang, H., Qu, X., Su, Q., Zheng, L., & Chen, Q. (2020) · Transboundary and Emerging Diseases · doi:10.1111/tbed.13902 · Cited by 25
Artificial intelligence and machine learning to enhance critical mineral deposit discovery
Davies, R. S., Trott, M., Georgi, J., & Farrar, A. D. (2025) · Geosystems and Geoenvironment · doi:10.1016/j.geogeo.2025.100361 · Cited by 29
Viniski, A. D., Barddal, J. P., Britto, A. D. S., Enembreck, F., & de Campos, H. V. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114890 · Cited by 24
E-learning Recommendation System Based on Cloud Computing
Rahhali, M., Oughdir, L., Jedidi, Y., Lahmadi, Y., & Khattabi, M. Z. E. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-6893-4_9 · Cited by 24
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health
Gyrard, A., & Boudaoud, K. (2022) · Applied Sciences · doi:10.3390/app12199712 · Cited by 24
Alrashidi, M., Selamat, A., Ibrahim, R. B., & Krejcar, O. (2023) · IEEE Access · doi:10.1109/access.2023.3276988 · Cited by 24
“Reference [41] examined the implementation domain for RSs and the use of machine learning approaches using a SLR process.”
MethodKumaran, S. K., Dogra, D. P., & Roy, P. P. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.057 · Cited by 24
Machine Learning Algorithms for Improved Product Design User Experience
Wang, X., & Hu, B. (2024) · IEEE Access · doi:10.1109/access.2024.3442085 · Cited by 23
“Machine learning algorithms empower designers and developers with the ability to extract insights from vast datasets, predict user behavior, and tailor experiences to individual preferences with unprecedented accuracy and efficiency [2], [3], [4].”
BackgroundMachine learning in emotional intelligence studies: a survey
Dollmat, K. S. B., & Abdullah, N. A. (2021) · Behavior and Information Technology · doi:10.1080/0144929x.2021.1877356 · Cited by 15
“To make sense of the available online data and user ’ s response, ML is used to create models that are later implemented in various analytical studies such as learning analytics (Cen et al. 2016), video analytics (Portugal, Alencar, and Cowan 2018) and web analytics.”
MethodBachinger, F., Kronberger, G., & Affenzeller, M. (2021) · IET Collaborative Intelligent Manufacturing · doi:10.1049/cim2.12009 · Cited by 26
“example, to predict customer preferences and behaviour [23], in predictive maintenance strategies to reduce downtimes and cost [24], to control complex machine state dynamics in manufacturing, or to predict product quality [25].”
BackgroundShafiee, S. (2024) · Procedia CIRP · doi:10.1016/j.procir.2023.08.062 · Cited by 23
Speeding up the development of solid state electrolyte by machine learning
Hu, Q., Chen, K., Li, J., Zhao, T., Liang, F., & Xue, D. (2024) · Next Energy · doi:10.1016/j.nxener.2024.100159 · Cited by 22
Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation
Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3091426 · Cited by 22
AutoML classifier clustering procedure
Koren, O., Hallin, C. A., Koren, M., & Issa, A. A. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22718 · Cited by 22
“26 The knn 14,40 algorithm is a simple yet effective classification algorithm (research that describes and examines the efficiency of additional classification algorithms on a few data sets can be found in Ali et al. 43 ).”
MethodMa, C., Xu, Q., & Li, B. (2021) · Library Hi Tech · doi:10.1108/lht-01-2021-0006 · Cited by 22
Chang, S. Y., Wu, H., Yan, K., Chen, X., Huang, S. C.-H., & Wu, Y. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2023.3278111 · Cited by 22
“…to the prior information as follows: ( i ) collaborative filtering (CF) RSs based on the historical information about users’ interactions with items and ( ii ) content-based (CB) RSs based on the extended prior information related to the properties of items and characteristics of users [18], [19].”
Toward a Knowledge-based Personalised Recommender System for Mobile App Development
Abu-Salih, B., Alsawalqah, H. I., Elshqeirat, B., Issa, T., Wongthongtham, P., & Premi, K. K. (2021) · JUCS - Journal of Universal Computer Science · doi:10.3897/jucs.65096 · Cited by 22
BTR: A Feature-Based Bayesian Task Recommendation Scheme for Crowdsourcing System
Dai, W., Wang, Y., Ma, J., & Jin, Q. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.2986836 · Cited by 22
“As mentioned in [24], the ensemble-based method is one of the most researched approaches in machine learning-based recommender system development, which has several ways of being implemented (e.g., bagging, boosting, and random forest).”
MethodInfluentialCredit Risk Prediction in Peer-to-Peer Lending with Ensemble Learning Framework
Chen, S., Wang, Q., & Shuan, L. (2019) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832412 · Cited by 21
Khodabandehlou, S., Golpayegani, A. H., & Rahman, M. Z. (2020) · Data Technologies and Applications · doi:10.1108/dta-04-2020-0094 · Cited by 20
Sentiment Analysis for POI Recommender Systems
Mishra, R. K., Urolagin, S., & Jothi, J. A. A. (2020) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt51279.2020.9320885 · Cited by 20
“Iven Portugal et al. [ [14] have shown in their research to use the machine learning techniques for the recommendation systems and what kind of algorithms can be applied for different types of recommendation systems.”
MethodSession context data integration to address the cold start problem in e-commerce recommender systems
Esmeli, R., Abdullahi, H., Bader-El-Den, M. B., & Can, A. S. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114339 · Cited by 20
Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
Lamba, D., Hsu, W. H., & Alsadhan, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00023-9 · Cited by 26
“A classic example of a machine learning application is a recommender system [68][69].”
BackgroundEvaluation of a machine learning classifier for metamodels
Nguyen, P. T., Rocco, J. D., Iovino, L., Ruscio, D. D., & Pierantonio, A. (2021) · Software & Systems Modeling · doi:10.1007/s10270-021-00913-x · Cited by 19
“An ML algorithm attempts to acquire real-world knowledge by autonomously simulating human’s learning activities [66].”
BackgroundA machine learning approach to analyse and predict the electric cars scenario: The Italian case
Miconi, F., & Dimitri, G. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279040 · Cited by 19
A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches
Yeh, J.-Y., & Wang, Y. (2023) · Journal of Global Information Management · doi:10.4018/jgim.327277 · Cited by 21
“8 ML ML is the science of computer algorithms to recognize and acquire information from the real world and improve the execution of assignments based on new information (Portugal et al., 2018).”
BackgroundHussain, M., Zhang, T., Dwight, R. A., & Jamil, I. (2024) · Journal of Pipeline Science and Engineering · doi:10.1016/j.jpse.2024.100178 · Cited by 21
Accelerated Singular Value Decomposition (ASVD) using momentum based Gradient Descent Optimization
Raghuwanshi, S. K., & Pateriya, R. K. (2018) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.03.012 · Cited by 19
“The basic concept behind the working of collaborative filtering is: if two users share a common interest, they are more likely to share those preferences in future as well (Ivens and Alencar Paulo, 2015), (Ricci et al., 2011).”
Ravi, M., Negi, A., Bommi, N. S., & Rouf, N. (2025) · IETE Technical Review · doi:10.1080/02564602.2025.2512086 · Cited by 18
Sahoh, B., Kliangkhlao, M., & Kittiphattanabawon, N. (2022) · Sensors · doi:10.3390/s22051925 · Cited by 18
“It learns such problem solving using experience provided by engineers and practitioners and improves learning performance according to varieties of affairs [21].”
BackgroundRecommendation Systems: Types, Applications, and Challenges
Patel, D., Patel, F., & Chauhan, U. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/130168 · Cited by 18
Karthiyayini, J., & Anandhi, R. J. (2024) · SN Computer Science · doi:10.1007/s42979-024-02667-x · Cited by 19
“In the field of RS, ML algorithms are utilized to provide customized recommendations to users [53, 93].”
A Machine Learning Based Approach for Recommending Courses at Graduate Level
Britto, J., Prabhu, S. N., Gawali, A., & Jadhav, Y. (2019) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit46314.2019.8987815 · Cited by 18
A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild
Generosi, A., Ceccacci, S., Faggiano, S., Giraldi, L., & Mengoni, M. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050622 · Cited by 18
“In the last few years there was significant improvement especially over the collaborative filtering approaches thanks to the advance in the field of machine learning and deep learning techniques [8].”
BackgroundGunathilaka, T. M. A. U., Manage, P. D., Zhang, J., Li, Y., & Kelly, W. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200474 · Cited by 17
He, Y., Kou, F., Wang, X. Y., Zhu, N., Song, Y., Chu, Y., Shi, S., Liu, M., & Chen, X. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108723 · Cited by 17
Predicting coffee yield based on agroclimatic data and machine learning
Aparecido, L. E. D. O., Lorençone, J. A., Lorençone, P. A., Torsoni, G. B., de Lima, R. F., & de Moraes, J. R. D. S. C. (2022) · Theoretical and Applied Climatology · doi:10.1007/s00704-022-03983-z · Cited by 19
“Machine learning algorithms, such as multiple linear regression (MLR), provide an efficient and accurate analysis (Portugal et al. 2018), being used in several areas, such as health (Abdelaziz et al. 2018; Yamashita et al. 2022), climatology (Cai et al. 2019; Guleryuz 2022), and agriculture (Liakos…”
MethodA Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach
Nilashi, M., Abumalloh, R. A., Alrizq, M., Almulihi, A. H., Alghamdi, O., Farooque, M. M. J., Samad, S., Mohd, S. B., & Ahmadi, H. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/7372849 · Cited by 17
Machine Learning Approaches in Advancing Perovskite Solar Cells Research
Subba, S., Rai, P., & Chatterjee, S. (2024) · Advanced Theory and Simulations · doi:10.1002/adts.202400652 · Cited by 19
Soft computing techniques in multi-criteria recommender systems: A comprehensive review
Anwar, K., Wasid, M., Zafar, A., Ganaie, M. A., & Iqbal, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112579 · Cited by 18
Mathematical Models for Optimization of Anaerobic Digestion and Biogas Production
Appala, V. N. S. G., Pandhare, N. N., & Bajpai, S. (2022) · Energy, environment, and sustainability · doi:10.1007/978-981-16-8682-5_21 · Cited by 17
Chen, H., Peng, W., Nie, C., Xu, G., & Lei, J. (2021) · Energy Technology · doi:10.1002/ente.202100045 · Cited by 16
The future of social entrepreneurship: modelling and predicting social impact
Zulkefly, N. A., Ghani, N. B. A., Chin, C. P.-Y., Hamid, S. B., & Abdullah, N. A. (2021) · Internet Research · doi:10.1108/intr-09-2020-0497 · Cited by 19
Survey of Different Recommendation Systems to Improve the Marketing Strategies on E-commerce
Sethi, V., & Kumar, R. (2022) · International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · doi:10.1109/com-it-con54601.2022.9850543 · Cited by 16
Exploring Smart Contract Recommendation: Towards Efficient Blockchain Development
Jiang, Z., Zheng, Z., Chen, K., Luo, X., Tang, X., & Li, Y. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3202081 · Cited by 16
“Portugal et al. [37] investigated the trend and open questions in the use or research of machine learning algorithms in recommender systems.”
BackgroundRocco, J. D., Sipio, C. D., Nguyen, P. T., Ruscio, D. D., & Pierantonio, A. (2022) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1145/3550355.3552459 · Cited by 16
“DL systems work by simulating the learning activities of humans [25], being capable of autonomously learning meaningful patterns from real-world examples [8, 36].”
Backgroundda Silva, V. C., Gorgulho, B. M., Marchioni, D. M. L., Matos, S. M. A., Giatti, L., de Araújo, T. A., Alonso, A. C., Santos, I. S., Lotufo, P. A., & Benseñor, I. M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192214934 · Cited by 18
“items or services, such as books, music, transportation, and even people [4].”
BackgroundVoting Classifier-Based Crop Recommendation
Bandi, R., Likhit, M. S. S., Reddy, S. R., Bodla, S. R., & Venkat, V. S. (2023) · SN Computer Science · doi:10.1007/s42979-023-01995-8 · Cited by 16
“Using historical data sets, the model is trained to predict future outcomes based on prior experience [16].”
MethodEnhanced Experiences: Benefits of AI-Powered Recommendation Systems
Carole, K. S., Armand, T. P. T., & Kim, H. C. (2024) · International Conference on Advanced Communications Technology (ICACT) · doi:10.23919/icact60172.2024.10471918 · Cited by 16
Are we ready for automated deep brain stimulation programming?
Cole, E. R., & Miocinovic, S. (2025) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2025.107347 · Cited by 16
A Recommender System Based on Hierarchical Clustering for Cloud e-Learning
Pireva, K., & Kefalas, P. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-66379-1_21 · Cited by 16
Vimala, S. V., & Vivekanandan, K. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-0708-9 · Cited by 16
“In this context, the collaborative filtering process is considered as the method used for manipulating or filtering products and services based on the sentiments of a community of people [5].”
MethodRecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers
Bunnell, L., Osei-Bryson, K.-M., & Yoon, V. (2019) · Information Systems Frontiers · doi:10.1007/s10796-019-09935-9 · Cited by 18
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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