The DOI matches its Crossref record; 158 of the 175 references it cites were found in a registry or an index, and 17 are not checked yet.
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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Cites
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.”
MethodJain, 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…”
BackgroundAdomavičius, 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].”
BackgroundSoftware 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
Shvachko, K. V., Kuang, H., Radia, S., & Chansler, R. J. (2010) · Digest of papers - IEEE Symposium on Mass Storage Systems · doi:10.1109/msst.2010.5496972 · Cited by 4,911 · cited 1×
“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.”
MethodFriedman, 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…”
BackgroundSupervised 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.”
BackgroundUnderstanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Research works printed but not linked: 14
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- Bagchi, 2015, 50, 229
- Burhans, 2004, 1, 82
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Cited by
Sorted by citations
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
“In addition, the use of shallow features leads to poor performance on incremental learning or unsupervised learning [40,41] .”
BackgroundHuang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023) · Computers & Education · doi:10.1016/j.compedu.2022.104684 · Cited by 605
Cubric, M. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101257 · Cited by 416
“Specific AI-technology reviews, but with no links to B&M subject e.g. [28].”
Recommendation system based on deep learning methods: a systematic review and new directions
Da’u, A., & Salim, N. B. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09744-1 · Cited by 433
“Alencar and Cowan (2018), used an SLR method to analyze the utilization of machine learning methods and their application domain for RS.”
MethodRebooting employees: Upskilling for artificial intelligence in multinational corporations
Jaiswal, A., Arun, C. J., & Varma, A. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1891114 · Cited by 382
“1 Further, AI itself comprises a set of algorithms which depends on data for executing the responses (Jaiswal & Arun, 2021; Portugal et al., 2018).”
Background
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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