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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.”
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MethodTransforming collaborative filtering into supervised learning
Braida, F., Mello, C. E., Pasinato, M. B., & Zimbrão, G. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.023 · Cited by 28
Second Order Online Collaborative Filtering
Lu, J. J., Hoi, S. C. H., Wang, J., & Zhao, P. (2013) · DR-NTU (Nanyang Technological University) · Cited by 28
TRUPI: Twitter Recommendation Based on Users’ Personal Interests
Elmongui, H. G., Mansour, R., Morsy, H., Khater, S., El-Sharkasy, A., & Ibrahim, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18117-2_20 · Cited by 28
Exploration of methodologies to improve job recommender systems on social networks
Diaby, M., Viennet, E., & Launay, T. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0227-z · Cited by 25
Reciprocal and Heterogeneous Link Prediction in Social Networks
Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y. S., Compton, P., & Mahidadia, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30220-6_17 · Cited by 25
Real-time clothes comparison based on multi-view vision
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Tutorial on application-oriented evaluation of recommendation systems
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Minimal Shrinkage for Noisy Data Recovery Using Schatten-p Norm Objective
Kong, D., Zhang, M., & Ding, C. H. Q. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40991-2_12 · Cited by 21
Cloud readiness assessment framework and recommendation system
Alemeye, F., & Getahun, F. (2015) · doi:10.1109/afrcon.2015.7331995 · Cited by 20
Two notes on machine “Learning”
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“Machine learning has been studied since the late 1950s [40], with the emergence of the field of AI.”
BackgroundAutomatic movie ratings prediction using machine learning
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Case Studies in Association Rule Mining for Recommender Systems.
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The Role of Machine Learning in Business Optimization
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Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers
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“Lee & Tseng, 2012) Kernel Methods 25 22 (Devi & Venkatesh, 2013; Dinuzzo et al., 2011; X. Li & Chen, 2013; X. Li et al., 2014; Agarwal, 2011; Banerjee et al., 2012; Brouard & Pomot, 2016; Diaby et al., 2013, 2014; Ghazarian & Nematbakhsh, 2015; Jun, 2005; Kong et al., 2013; Pecli et al., 2015; Pronoza et al., 2016; I.…”
A meta-learning framework for algorithm recommendation in software fault prediction
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Context and Intention-Awareness in POIs Recommender Systems
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Evaluating software quality in use using user reviews mining
Leopairote, W., Surarerks, A., & Prompoon, N. (2013) · doi:10.1109/jcsse.2013.6567355 · Cited by 17 · cited 3×
“Association Rule 4 2 (Leopairote et al., 2013; X.-Z. Zhang, 2007) Kernel Methods 4 4 (Devi & Venkatesh, 2013; Dinuzzo et al.”
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Devi, M. K. K., & Venkatesh, P. (2011) · Future Generation Computer Systems · doi:10.1016/j.future.2011.05.011 · Cited by 16
On the Design of Social Voting Recommendation Applications
Tsapatsoulis, N., Agathokleous, M., Djouvas, C., & Mendez, F. (2015) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213015500098 · Cited by 15 · cited 4×
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InfluentialAutomatic image tagging and recommendation via PARAFAC2
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Web Personalization in Intelligent Environments
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A Text Clustering Approach of Chinese News Based on Neural Network Language Model
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Customising WAP-based information services on mobile networks
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A Novel Ontology and Machine Learning Inspired Hybrid Cardiovascular Decision Support Framework
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“Decision Tree 14 13 (Alemeye & Getahun, 2015; Aouay et al., 2014; Banerjee et al., 2012; Bellogı́n et al., 2011; Caraballo et al., 2014; Costa et al., 2013, 2013; Haiduc et al., 2013; Hernández del Olmo et al., 2009; Hussain et al., 2015; W.-P. Lee & Lu, 2003; T. Liu et al., 2011; Musto et al., 2010; Wei et al., 2011)…”
Neural expert networks for faster combined collaborative and content-based recommendation
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“Movie [5] [5] [5] [14] [36] [39] [53] [53] [60] [60] 10”
Global Optimization for Advertisement Selection in Sponsored Search
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“Today, ML algorithms are used in several areas besides computer science, including business [6], advertising [16] and medicine [32].”
MethodAouay, S., Jamoussi, S., & Gargouri, F. (2014) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2014.7073243 · Cited by 11 · cited 10×
“, 2009; Zhai & Li, 2015) k Nearest Neighbors 11 10 (Aouay et al., 2014; Castro-Herrera et al., 2009; Das Dôres et al., 2016; Hernández del Olmo et al., 2009; Kong et al., 2013; Liang et al., 2014; R. Liu et al., 2014; Marović et al., 2011; McLaughlin & Herlocker, 2004; Pecli et al., 2015) Latent Semantic Analysis 7 4…”
BackgroundInfluentialExploration / Exploitation Trade-Off in Mobile Context-Aware Recommender Systems
Bouneffouf, D., Bouzeghoub, A., & Gançarski, A. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35101-3_50 · Cited by 11
Difference Factor’ KNN Collaborative Filtering Recommendation Algorithm
Liang, W., Lu, G., Ji, X., Li, J., & Yuan, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14717-8_14 · Cited by 11 · cited 3×
“, 2009; Zhai & Li, 2015) k Nearest Neighbors 11 10 (Aouay et al., 2014; Castro-Herrera et al., 2009; Das Dôres et al., 2016; Hernández del Olmo et al., 2009; Kong et al., 2013; Liang et al., 2014; R. Liu et al., 2014; Marović et al., 2011; McLaughlin & Herlocker, 2004; Pecli et al., 2015) Latent Semantic Analysis 7 4…”
BackgroundLahlou, F. Z., Benbrahim, H., Mountassir, A., et al. (2013) · doi:10.1109/aiccsa.2013.6616512 · Cited by 10
TRTML - A Tripleset Recommendation Tool Based on Supervised Learning Algorithms
Caraballo, A. A. M., Arruda, N., Nunes, B. P., Lopes, G. R., & Casanova, M. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11955-7_58 · Cited by 10
Computational intelligence meets the NetFlix prize
Meuth, R. J., Robinette, P., & Wunsch, D. C. (2008) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633869 · Cited by 9
SVM-based association rules for knowledge discovery and classification
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Discovering Social Network to Improve Recommender System for Group Learning Support
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“, 2015) Pearson Correlation 2 2 (Halder et al., 2014; Wan et al., 2009) Staked Regression 2 1 (Q.”
A Text Classification based method for context extraction from online reviews
Lahlou, F. Z., Mountassir, A., Benbrahim, H., & Kassou, I. (2013) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2013.6560804 · Cited by 9
On the Performance of Distributed Clustering Algorithms in File and Streaming Processing Systems
Ericson, K., & Pallickara, S. L. (2011) · doi:10.1109/ucc.2011.15 · Cited by 8
The task of guiding in adaptive recommender systems
del Olmo, F. H., Gaudioso, E., & Martin, E. H. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.070 · Cited by 8
RedTweet: recommendation engine for reddit
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Aspect-Based Restaurant Information Extraction for the Recommendation System
Pronoza, E., Викторовна, Я. Е., & Volskaya, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-43808-5_28 · Cited by 8
Mining Intelligence and Knowledge Exploration
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Learning to Recommend Based on Slope One Strategy
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“MAE 123 20 (Bauer & Nanopoulos, 2014; Braida et al., 2015; Castro-Herrera et al., 2009; Degemmis et al., 2007; Devi & Venkatesh, 2013; T.-K. Fan & Chang, 2010; Ghazarian & Nematbakhsh, 2015; Hofmann, 2004; Jung & Lee, 2004; Karahodza & Donko, 2015; Q. Liu et al., 2014; J. Lu et al., 2013; McLaughlin & Herlocker, 2004;…”
SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK
Shinde, A., Haghnevis, M., Janssen, M. A., Runger, G. C., & Janakiram, M. (2013) · International Journal of Innovation and Technology Management · doi:10.1142/s0219877013400191 · Cited by 6 · cited 2×
“Decision Tree [21] [36] [39] [55] [57] 5”
A multi-stage learning framework for intelligent system
Kao, C.-Y., & Fahn, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.046 · Cited by 6
EPG Content Recommendation in Large Scale: A Case Study on Interactive TV Platform
Zibriczky, D., Petres, Z., Waszlavik, M., & Tikk, D. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.142 · Cited by 6
MapReduce implementation of Variational Bayesian Probabilistic Matrix Factorization algorithm
Tewari, N. C., Koduvely, H. M., Guha, S., Yadav, A. K., & David, G. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691747 · Cited by 6
Collaborative Filtering via Group-Structured Dictionary Learning
Szabó, Z., Póczos, B., & Lörincz, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28551-6_31 · Cited by 6
Recommending POIs Based on the User’s Context and Intentions
Costa, H., Furtado, B., Pires, D., Macedo, L., & Cardoso, A. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-38061-7_17 · Cited by 6
Research of information recommendation system based on reading behavior
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Kulkarni, S. (2012) · FedUni ResearchOnline (Federation University Australia) · doi:10.4018/978-1-4666-1833-6 · Cited by 6 · cited 1×
“Some examples of classification can be found in (Shalev-Shwartz & Ben-David, 2013) and (Kulkarni, 2012), although the field still does not have any standard classification.”
Background(Partial) user preference similarity as classification-based model similarity
Bouza, A., & Bernstein, A. (2014) · Semantic Web · doi:10.3233/sw-130099 · Cited by 5
Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage
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TISA: Topic Independence Scoring Algorithm
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Identification of category associations using a multilabel classifier
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Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems
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Constructing Topic Models of Internet of Things for Information Processing
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“Accuracy 49 26 (Alemeye & Getahun, 2015; Banerjee et al., 2012; Bellogı́n et al., 2011; Bjelica, 2010; Cai et al., 2012; Castro-Herrera et al., 2009; Halder et al., 2014; Hussain et al., 2015; Jin et al., 2005; Kong et al., 2013; Krzywicki et al., 2015; W.-P. Lee & Lu, 2003; T.-J. Lee & Tseng, 2012; Q.-C. Li et al., 2…”
InfluentialA multi-institutional project-centric framework for teaching AI concepts
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“Not sufficiently describing the approach [38] [51] [52] 3”
BackgroundAn entertainment recommendation system using the dynamics of user behavior over time
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Group Recommender Systems - Some Experimental Results
Padmanabhan, V., Kiran, P., & Sattar, A. R. (2013) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0004331003700376 · Cited by 3
Link Prediction Across Time via Cross-Temporal Locality Preserving Projections
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Intelligent Techniques in Recommendation Systems
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Self-Driving Cars - An AI-Robotics Challenge.
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The design and implementation of distributed mobile points of interest(POI) based on Mahout
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“Not sufficiently describing the approach [38] [51] [52] 3”
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Software Engineering: The Trend.
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Combining User-Based and Item-Based Models for Collaborative Filtering using Stacked Regression
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Feature enhanced time-aware recommender system
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Leveraging Semantic Web technologies for more relevant E-tourism Behavioral Retargeting
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Refine Social Relations and Differentiate the Same Friends’ Influence in Recommender System
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Lawyer information integration and recommendation by multi-source information validation
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An Online Bayesian Networks Model for E-Commercial Personalized Recommendation System
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“Type of Machine Learning Algorithm Number of ML algorithms Number of studies Studies Ensemble 22 14 (Aouay et al., 2014; Bar et al., 2013; Biancalana et al., 2011; Braida et al., 2015; Buabin, 2012; Elmongui et al., 2015; T.-K. Fan & Chang, 2010; Islam et al., 2015; Kao & Fahn, 2013; Middleton et al., 2004; Szymański…”
BackgroundInfluentialTwo Algorithms Under Stochastic Gradient Descent Framework for Recommender Systems
Huang, T.-H., & Nikulin, V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13186-3_21 · Cited by 1
Catlinks - a category clustering algorithm based on multi-class regression
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“Unsupervised learning 46 24 (Bar et al., 2013; Bjelica, 2010; Bouneffouf et al., 2012; Buettner, 2016; Degemmis et al., 2007; Devi & Venkatesh, 2013; Elmongui et al., 2015; Z. Fan et al., 2016; Ghazarian & Nematbakhsh, 2015; Halder et al., 2014; Hassan et al., 2010; T.-J. Lee & Tseng, 2012; J. Li & Zaı̈ane, 2004; X. L…”
BackgroundInfluentialAgosti, M., Thanos, C., & Esposito, F. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-15850-6
Flagged first, then by citations
Research works printed but not linked: 14
- Aouay, 2014, 2014, 523
- Bagchi, 2015, 50, 229
- Burhans, 2004, 1, 82
- Jin, 2005, 1, 213
- Jun, 2005, 3512, 349
- Li, 2008, 3, 1626
- Liu, 2011, 4, 1909
- Montañés, 2009, 497, 173
- Murfi, 2009, 497, 201
- Nicol, 2014, 2, 1390
- Pecli, 2015, 1, 295
- Yeh, 2010, 1, 209
- Zhang, 2007, 6, 3506
- Zhang, 2007, 7, 4113
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
About these checks
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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