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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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Cites
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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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“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.”
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“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…”
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“The field of RS has its origins in the mid-1990s with the introduction of Tapestry [23], the first RS.”
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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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