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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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“In addition, the use of shallow features leads to poor performance on incremental learning or unsupervised learning [40,41] .”
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Recommendation system based on deep learning methods: a systematic review and new directions
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“1 Further, AI itself comprises a set of algorithms which depends on data for executing the responses (Jaiswal & Arun, 2021; Portugal et al., 2018).”
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“…tensor factorization have been proposed, such as DeRec [99], SADE [101], DRMF [100], DLNN [102], TFCF [114], CoTF [115], and WHBPR [117], and these models exhibit higher recommendation precision compared with state-of-the-art recommendation algorithms [95]–[97], [99], [105], [118], [119], [121].”
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“There are many surveys that have already discussed the basics of ML and DL techniques, such as [63], [69], [70], [73], [74], [75].”
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“However, it has been proved that joint over recommendation and transmission will bring greater improvement to the QoE [13].”
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“For example, we limited the selected papers to the studies published in the Scopus database, which are deemed as the one of the most comprehensive and powerful databases according to [15], however, other databases can also be explored.”
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Chinnasamy, P., Wong, W., Raja, A. A., Khalaf, O. I., Kiran, A., & Babu, J. C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22844 · Cited by 73
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“The authors in [96] conducted systematic review to investigate which ML algorithm support RS and identified that Bayesian, Decision Trees, Neighbor-based, Neural networks, Clustering and Gradient descent-based algorithms are applied mostly in RS.”
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Freire, M. N., & de Castro, L. N. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01522-8 · Cited by 68
“It can also be found approaches that build ensembles, combining different techniques forming a bidirectional recommender system [40,72,84].”
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Career Recommendation Systems using Content based Filtering
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“[1][2] II.”
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“In this way, they are able to simulate humans’ cognitive functions, aiming to acquire real-world knowledge autonomously [5].”
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“[125] surveyed 26 articles on machine learning (ML) algorithms for RSs and summarized the areas of software engineering that can help answer open issues in the ield.”
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“2) RMSE = ((1/N) ∑N 1 (yi − ŷi )(2))1/2, root mean square error [27], the smaller the better.”
BackgroundInfluentialCollaborative filtering recommendation algorithm integrating time windows and rating predictions
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“Portugal et al. [15] and Campos et al. [16] found that personalized recommendations through machine learning have certain advantages.”
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“The procedure followed in this systematic review is based on (Portugal et al., 2018) and is addressing the following goals: 1.”
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“Existing RS system surveys do no address RS for happiness yet [7] (machine learning-based RS published in 2018) [8] [9] [10]. well-being RS (published in 2019) [4] illustrates the emerging research need.”
BackgroundLink prediction in recommender systems based on vector similarity
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“This technology is so critical that the competitive advantage of Amazon’s book or product recommendations, Spotify’s ability to discover music taste, and Netflix’s movie recommendation precision are the core of their success (Portugal, Alencar, and Cowan, 2018).”
BackgroundA Systematic Mapping Review on MOOC Recommender Systems
Uddin, I., Imran, A. S., Muhammad, K., Fayyaz, N., & Sajjad, M. (2021) · IEEE Access · doi:10.1109/access.2021.3101039 · Cited by 56
“A number of surveys are conducted in the domain of eLearning RS [45]–[48], RS in general [49]–[51], review of the factors that affecting MOOC quality [52], but to the best of our knowledge only 3 survey focuses on MOOCRS [11], [15], [53].”
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“pervised machine learning, there is usually a need for a predicted (or ”dependent”) variable that the machine-learning model is supposed to predict, especially if the prediction is about classes or categories of data [73, 125, 179].”
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ML based sustainable precision agriculture: A future generation perspective
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“Over the last decades, large effort has been put into optimizing different algorithms to deliver the most accurate–or relevant–recommendations [24, 36].”
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“Machine learning has rapidly permeated numerous domains over the last two decades, encompassing daily life[1, 2] and scientific research[3, 4, 5], significantly changed our way of living and studying through the exploitation of vast datasets.”
A recommender system for component-based applications using machine learning techniques
Fernández‐García, A. J., Iribarne, L., Corral, A., Criado, J., & Wang, J. Z. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.10.019 · Cited by 51
“For these reasons, the use of recommendation models [31] to forecast the use of resources, anticipate user needs, or to make recommendations based on their behavior (and that of other users, including a situation context) are becoming more frequent.”
BackgroundAn overview of Machine Learning Technologies and their use in E-learning
Farhat, R., Mourali, Y., Jemni, M., & Ezzedine, H. (2020) · International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA) · doi:10.1109/octa49274.2020.9151758 · Cited by 50
“We know that if we give more experiences (E) with a defined task (T) to a machine, its performance (P) improves [12].”
BackgroundSuraña‐Sánchez, C., & Aramendia-Muneta, M. E. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13027 · Cited by 67
Liu, Z., Guo, S., Wang, L., Du, B., & Pang, S. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.042 · Cited by 49
Casillo, M., Gupta, B. B., Lombardi, M., Lorusso, A., Santaniello, D., & Valentino, C. (2022) · Electronics · doi:10.3390/electronics11071003 · Cited by 47
Automated Classification of Metamodel Repositories: A Machine Learning Approach
Nguyen, P. T., Rocco, J. D., Ruscio, D. D., Pierantonio, A., & Iovino, L. (2019) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1109/models.2019.00011 · Cited by 46
“Thanks to this characteristic, ML algorithms find their applications in various domains, including web search by learning from a user’s longterm search history [48], recommender systems [41], and selfdriving vehicles [50].”
BackgroundApplication of machine learning to the prediction of postoperative sepsis after appendectomy
Bunn, C., Kulshrestha, S., Boyda, J., Balasubramanian, N., Birch, S., Karabayir, İ., Baker, M. S., Luchette, F. A., Modave, F. P., & Akbilgiç, O. (2020) · Surgery · doi:10.1016/j.surg.2020.07.045 · Cited by 46
Big Data: una exploración de investigaciones, tecnologías y casos de aplicación
Hernández-Leal, E. J., Méndez, N. D. D., & Moreno, J. (2017) · TecnoLógicas · doi:10.22430/22565337.685 · Cited by 44
Blomster, M., & Koivumäki, T. (2021) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00547-y · Cited by 50
Hoepner, A. G. F., McMillan, D. G., Vivian, A. J., & Simen, C. W. (2020) · European Journal of Finance · doi:10.1080/1351847x.2020.1847725 · Cited by 43
“…repeatedly feature in reviews of machine learning algorithms although the majority of machine learning research is conducted with neural networks, support vector machines and other classification algorithms (Basha and Rajput2019 ; Mahdavinejad et al. 2018; Portugal, Alencar, and Cowan 2018).”
Suvarna, B., & Balakrishna, S. (2024) · Fashion and Textiles · doi:10.1186/s40691-024-00382-y · Cited by 42
“Enough research has been done related to the RS. Initially, machine learning models were utilized in these systems (Balaji et al., 2021; Portugal et al., 2018).”
MethodAn adaptive doctor-recommender system
Waqar, M., Majeed, M. N., Dawood, H., Daud, A. Y., & Aljohani, N. R. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1625441 · Cited by 42
“Further research work is going on to further enhance the capability of recommender systems in healthcare (Portugal, Alencar, and Cowan 2018).”
BackgroundEstimating city-level poverty rate based on e-commerce data with machine learning
Wijaya, D. R., Paramita, N. L. P. S. P., Uluwiyah, A., Muztahid, M. R., Zahara, A., & Puspita, D. R. (2020) · Electronic Commerce Research · doi:10.1007/s10660-020-09424-1 · Cited by 42
Domain-Specific Architectures: Research Problems and Promising Approaches
Krishnakumar, A., Ogras, U. Y., Mărculescu, R., Kishinevsky, M., & Mudge, T. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3563946 · Cited by 48
“For instance, machine learning and AI are extensively being used for image processing, scheduling, recommendation systems, spam iltering, stock market analysis, and medical applications [120, 134, 142, 183].”
Pires, F., Leitão, P., Moreira, A. P., & Ahmad, B. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103884 · Cited by 39
Long, X., Gu, X., Lu, C., Li, Z., Ma, Y., & Jian, Z. (2023) · Cold Regions Science and Technology · doi:10.1016/j.coldregions.2023.103928 · Cited by 39
Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
Sundermann, C. V., Domingues, M. A., Sinoara, R. A., Marcacini, R. M., & Rezende, S. O. (2019) · Information · doi:10.3390/info10020042 · Cited by 39
“Some systematic reviews were published in the recommender system area, with or without contextual information, like [81–84], but none of them considered opinion mining and context together.”
BackgroundTowards a knowledge based Explainable Recommender Systems
Samih, A., Adadi, A., & Berrada, M. (2019) · International Conference on Big Data and Internet of Things (BDIOT) · doi:10.1145/3372938.3372959 · Cited by 38
“Indeed, RS are one of the most successful applications of ML techniques [7] and each algorithmic approach has adherents who claim it to be superior for some purpose.”
BackgroundVergaray, A. D., Herrera, J. C., Cornelio, J. B., Carranza, A. R. L., & Sánchez, C. F. P. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101295 · Cited by 38
Data-driven decision making based on evidential reasoning approach and machine learning algorithms
Fu, C., Xu, C., Xue, M., Liu, W., & Yang, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107622 · Cited by 41
Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey
Gupta, M., Jha, R. K., & Jain, S. (2022) · IEEE Access · doi:10.1109/access.2022.3148473 · Cited by 39
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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