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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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“Portugal et al. [6] discussed the categorization of the recommender system and its application of machine learning algorithms in detail.”
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“Intelligent recommendation systems in the TV domain are based on the classification rules connecting the time factors and the users’ preferences with the TV ratings [2,3].”
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“4 Performance comparison and analysis In order to evaluate the performance of the ISocialMF algorithm proposed in this paper, the algorithm is compared with social recommendation methods such as PMF ([23]), TrustPMF ([14]), SocialMF ([18]), PRM ([31]), EnSocialMF([25]), USSHMF([28]), and SocialIT ([3]).”
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Decentralized Innovation: Exploring the Impact of Blockchain Technology in Software DevelopmentPreprint
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A systematic review of similar Questions Retrieval Approaches
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A Study of Artificial Intelligence Effects on Human Resource Digitalisation: Digital Recruitment 5.0
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Machine Learning integrated resource management framework for construction projects
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“Currently, ML is used in manufacturing process (Paturi and Cheruku 2021), medical sector (Goecks et al. 2020; Portugal et al. 2018), business analytics (Adebiaye et al. 2023), weather forecasting (Moragane et al. 2024), automobile sector (Grigorescu et al. 2020) and in different aspects of the…”
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Quantum machine learning for predicting properties of van der Waals bilayers
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“For our evaluation, we selected a set of recommender algorithms from the literature [4]: Random, most popular (MP), UserKnn, ItemKnn, Group-based, Poisson Additive CoClustering (PaCo), and Bayesian Personalized Ranking with Matrix Factorization (BPRMF).”
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“There are supervised, unsupervised, semi-supervised, and reinforced learning methods [10].”
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“However, the literature of the ML field presents a high number of approaches and variations which makes difficult to establish a clear classification scheme for its algorithms [14].”
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Rysbayeva, G., & Zhang, J. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140206 · Cited by 1
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“Mostly related items are recommended to users (Portugal, Alencar, and Cowan 2018).”
BackgroundThe Impact of Covid 19 on Recommendation Platforms
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Optimized Detection of Ovarian Cancer Using Segmentation with FR-CNN Classification
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“Recommendation systems are a subclass of information filtering systems used to locate the most pertinent item to a particular user from a data pool [14].”
Product Recommendation Using Classification Algorithms
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Novel Framework for Proposed Intelligent Examination System
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Temporal information effect on personalized recommendation
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“Mostly Bayesian Algorithm, Decision Trees, Neural Networks are used for providing recommendations to the users [17].”
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Oladipo, F. O., Ogunsanya, F. B., & Ariwa, E. I. (2020) · International Journal of Computer Science and Engineering · doi:10.14445/23488387/ijcse-v7i12p103 · Cited by 1
Point of Interest Recommendation Acceleration Using Clustering
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“Many researchers have adopted different techniques to improve the accuracy of the recommendation systems [1].”
MethodLorençone, J. A., Aparecido, L. E. D. O., Lorençone, P. A., & de Moraes, J. R. D. S. C. (2021) · International Journal of Environmental Resilience Research and Science · doi:10.48075/ijerrs.v3i1.26255 · Cited by 1
Efficient Recommendation System Using Latent Semantic Analysis
Budhraj, R., Kherwa, P., Sharma, S., & Gill, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3071-2_50 · Cited by 1
DETERMINE RECOMMENDATION SYSTEMS TO SEARCH FOR BOOKS BY PREFERENCES OF WEB USERS
Козуля, М. М., & Sushko, V. (2021) · Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies · doi:10.20998/2079-0023.2021.02.12 · Cited by 1
“In [17], the authors reviewed 26 articles and found that in most cases, the recommendation systems use the Baew approach or the tree-based approach for movies, music, news, and products for web stores.”
BackgroundEfficient Recommender System for Kid’s Hobby Using Machine Learning
Lunawat, S. S., Rampurawala, A., Pujari, S., Thawal, S., Pangare, J., Thorat, C., & Munot, B. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-7088-6_29 · Cited by 1
Machine Learning Household Waste Disposal Behavior Related Factors
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The Advances in Recommendation Systems – Theoretical Analysis
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Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
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Maximum Entropy Exploration in Contextual Bandits with Neural Networks and Energy Based Models
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“In recent years, machine learning has been applied to solve a large array of concrete scientific and business problems (Silver et al., 2016; Portugal et al., 2018; Sarker, 2021).”
When to Push Ads: Optimal Mobile Ad Campaign Strategy Under Markov Customer Dynamics
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Techniques, benefits, and challenges of recommendation system in e-commerce: A literature review
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“This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities (Portugal et al., 2018).”
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“In recent researches numerous ML has been proposed based on the learning process it is classified as supervised, non-supervised learning approach and reinforcement learning approach [1].”
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A New Feature Selection Method Based on Class Association Rule
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Simple estimation of hierarchical positions and uncertainty in networks of asymmetric interactionsPreprint
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“Methods for estimation from pairwise comparisons are widely used in designing recommender systems [36, 37] or skill rating systems such as the Elo system in chess [38] or the more recent TrueSkill [39].”
MethodIntelligent Job and Career Recommender Systems: A Systematic Review v1Preprint
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“(ii) Machine learning algorithms can be implemented to allow the system to answer complex questions (Portugal et al., 2018).”
BackgroundPredicting United States National Basketball Game Spreads Using Machine Learning TechniquesPreprint
Jayasuriya, D., Liu, J. J., & Dow, K. E. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4766044 · Cited by 1
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A Study on Diagnosis of Parkinson’s Disease from Voice Dysphonias
Akyol, K. (2018) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2018.06.04
“Today, there are a large number of machine learning algorithms proposed in the literature [13].”
BackgroundLibX — An automated bibliotheca
Trivedi, Y., Joshi, S., Kansaria, K., & Vora, D. R. (2017) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2017.8389406
Renova: A Techno-Strategic Commodity Locator Inception
Abhigna, B. S., Chakraborty, S., & Arora, M. (2018) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2018.8441705
A Framework for Form Applications that Use Machine Learning
de Aguiar, G., & Vilain, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03493-1_80
Comparison of Recommender Systems in an Ed-Tech Application.Preprint
Baldominos, A., & Quintana, D. (2018) · arXiv (Cornell University)
A Decision-Tree Analysis of the Relationship between Social Development and Homicide Rates
Gutiérrez-García, J. O., Garza, A. G. D. S., Ramírez-Ramírez, L. L., Patiño, R., & Candela, E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-21248-3_49
Big data, privacy and security in smart grids
Kabalcı, E., & Kabalcı, Y. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-819710-3.00008-9
A Recommendation Mechanism based on Positive Preferences
Chang, C.-C., & Liu, J.-C. (2019) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts.2019.8850958
“Thank you, next”: A Call for Intentional Design
Garcia, M. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.292
Adaptive Hybrid Collaborative Filtering Recommendation System (AHCF)
Agboyi, R. (2019)
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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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