The use of machine learning algorithms in recommender systems: a systematic review

Portugal, Ivens; Alencar, Paulo; Cowan, Donald

doi:10.1016/j.eswa.2017.12.020

Cited by 743Recommender Systems and TechniquesInformation Retrieval and Search BehaviorAdvanced Bandit Algorithms Research

The use of machine learning algorithms in recommender systems: a systematic review

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Portugal, Ivens, Alencar, Paulo, Cowan, Donald·2018·Expert Systems with Applications

DOI 10.1016/j.eswa.2017.12.020

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The mathematics of recommender systems and interpolation capabilities of machine learning will enable recommendations on making secondary surveys cheaper with a minor loss in the value of acquired information.
“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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Background
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Collaborative Filtering for people-to-people recommendation in online dating: Data analysis and user trial
Krzywicki, A., Wobcke, W., Kim, Y. S., Cai, X., Bain, M., Mahidadia, A., & Compton, P. (2014) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2014.12.003 · Cited by 34 · see citing works
Risk-Aware Recommender Systems
Bouneffouf, D., Bouzeghoub, A., & Gançarski, A. L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42054-2_8 · Cited by 34 · cited 2× · see citing works
“These systems also play an important role in decision-making, helping users to maximize profits [15] or minimize risks [11].”
Background
A Semantic Content-Based Recommender System Integrating Folksonomies for Personalized Access
Lops, P., de Gemmis, M., Semeraro, G. M., Musto, C., Narducci, F., & Bux, M. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-02794-9_2 · Cited by 34 · cited 3× · see citing works
“, 2015) Graphical Model 17 15 (Yuan et al., 2014; Aouay et al., 2014; Banerjee et al., 2012; Costa et al., 2012, 2013; De Gemmis et al., 2008; Hernández del Olmo et al., 2009; Lops et al., 2009; Musto et al., 2010; Paparrizos et al., 2011; Pecli et al., 2015; Pronoza et al., 2016; S.-Z. Zhang et al., 2007; Xin et al.,…”
Client–Server Multitask Learning From Distributed Datasets
Dinuzzo, F., Pillonetto, G., & Nicolao, G. D. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2095882 · Cited by 30 · see citing works
Learning Collaborative Filtering and Its Application to People to People Recommendation in Social Networks
Cai, X., Bain, M., Krzywicki, A., et al. (2010) · Proceedings · doi:10.1109/icdm.2010.159 · Cited by 30 · see citing works
Error Analysis of Stochastic Gradient Descent Ranking
Chen, H., Tang, Y., Li, L., Yuan, Y., Li, X., & Tang, Y. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2217957 · Cited by 29 · see citing works
Dynamically-optimized context in recommender systems
Yap, G.-E., Tan, A., & Pang, H. (2005) · International Conference on Mobile Data Management (MDM) · doi:10.1145/1071246.1071289 · Cited by 29 · see citing works
A Health Social Network Recommender System
Song, I., Dillon, D., Goh, T. J., & Sung, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25044-6_29 · Cited by 29 · see citing works
Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews
Zhang, R., & Tran, T. T. (2010) · Journal of electronic commerce research · Cited by 29 · see citing works
Hybrid-ε-greedy for Mobile Context-Aware Recommender System
Bouneffouf, D., Bouzeghoub, A., & Gançarski, A. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30217-6_39 · Cited by 28 · cited 1× · see citing works
“For example, [12] describes a multi-armed Bandit algorithm with cosine measure.”
Method
Transforming 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 23 · see citing works
Real-time clothes comparison based on multi-view vision
Zhang, W., Begole, B., Chu, M., et al. (2008) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2008.4635727 · Cited by 23 · see citing works
Building Personalized Recommendation System in E-Commerce using Association Rule-Based Mining and Classification
Zhang, X. (2007) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2007.4370866 · Cited by 22 · see citing works
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 22 · see citing works
End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression
Das, S., Moore, T., Wong, W., Stumpf, S., Oberst, I., McIntosh, K., & Burnett, M. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2013.08.003 · Cited by 21 · see citing works
Tutorial on application-oriented evaluation of recommendation systems
Shani, G., & Gunawardana, A. (2013) · AI Communications · doi:10.3233/aic-130551 · Cited by 21 · see citing works
Cloud readiness assessment framework and recommendation system
Alemeye, F., & Getahun, F. (2015) · doi:10.1109/afrcon.2015.7331995 · Cited by 20 · see citing works
Two notes on machine “Learning”
Martens, H. H. (1959) · Information and Control · doi:10.1016/s0019-9958(59)80014-0 · Cited by 19 · cited 1× · see citing works
“Machine learning has been studied since the late 1950s [40], with the emergence of the field of AI.”
Background
Automatic movie ratings prediction using machine learning
Marović, M., Mihokovic, M., Mikša, M., Pribil, S., & Tus, A. (2011) · International Convention on Information and Communication Technology, Electronics and Microelectronics · Cited by 19 · see citing works
Case Studies in Association Rule Mining for Recommender Systems.
Smyth, B., McCarthy, K., Reilly, J. P., O’Sullivan, D., McGinty, L., & Wilson, D. C. (2005) · International Conference on Artificial Intelligence · Cited by 18 · see citing works
The Role of Machine Learning in Business Optimization
Apt, C. (2010) · International Conference on Machine Learning · Cited by 18 · see citing works
Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers
Verma, C. K., Hart, M., Bhatkar, S., Parker-Wood, A., & Dey, S. (2015) · IEEE Access · doi:10.1109/access.2015.2513000 · Cited by 18 · cited 3× · see citing works
“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
das Dôres, S. N., Alves, L., Ruiz, D. D., et al. (2016) · ACM Symposium on Applied Computing · doi:10.1145/2851613.2851788 · Cited by 18 · see citing works
Context and Intention-Awareness in POIs Recommender Systems
Costa, H., Furtado, B., Pires, D., Macedo, L., & Cardoso, A. (2012) · Cited by 17 · see citing works
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× · see citing works
“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 · see citing works
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× · see citing works
“Total retained [5] [7] [8] [9] [10] [12] [14] [19] [20] [21] [24] [25] [31] [36] [39] [41] [53] [55] [57] [58] [60] [61] [64] [64] [67] [68] 26”
Influential
Automatic image tagging and recommendation via PARAFAC2
Pantraki, E., & Kotropoulos, C. L. (2015) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2015.7324363 · Cited by 15 · see citing works
Web Personalization in Intelligent Environments
Castellano, G., Castellano, G., Jain, L. C., & Fanelli, A. M. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-02794-9 · Cited by 15 · see citing works
A Text Clustering Approach of Chinese News Based on Neural Network Language Model
Fan, Z., Chen, S., Zha, L., & Yang, J. (2014) · International Journal of Parallel Programming · doi:10.1007/s10766-014-0329-2 · Cited by 14 · see citing works
Customising WAP-based information services on mobile networks
Lee, W., & Lu, C.-C. (2003) · Personal and Ubiquitous Computing · doi:10.1007/s00779-003-0247-6 · Cited by 13 · see citing works
Neural expert networks for faster combined collaborative and content-based recommendation
Álvarez, S. A., Ruiz, C., Kawato, T., & Kogel, W. (2011) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-2011-0360 · Cited by 11 · cited 2× · see citing works
“Movie [5] [5] [5] [14] [36] [39] [53] [53] [60] [60] 10”
Global Optimization for Advertisement Selection in Sponsored Search
Cui, Q., Bai, F., Gao, B., & Liu, T. (2015) · Journal of Computer Science and Technology · doi:10.1007/s11390-015-1523-4 · Cited by 11 · cited 1× · see citing works
“Today, ML algorithms are used in several areas besides computer science, including business [6], advertising [16] and medicine [32].”
Method
Feature based link prediction
Aouay, 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× · see citing works
“, 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…”
BackgroundInfluential
Exploration / 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 · see citing works
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× · see citing works
“, 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…”
Background
Context extraction from reviews for Context Aware Recommendation using Text Classification techniques
Lahlou, F. Z., Benbrahim, H., Mountassir, A., et al. (2013) · doi:10.1109/aiccsa.2013.6616512 · Cited by 10 · see citing works
A Novel Ontology and Machine Learning Inspired Hybrid Cardiovascular Decision Support Framework
Hussain, A., Farooq, K., Luo, B., & Slack, W. V. (2015) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2015.122 · Cited by 10 · cited 3× · see citing works
“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)…”
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 · see citing works
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 · see citing works
SVM-based association rules for knowledge discovery and classification
Anaissi, A., & Goyal, M. L. (2015) · Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) · doi:10.1109/apwccse.2015.7476236 · Cited by 9 · see citing works
Discovering Social Network to Improve Recommender System for Group Learning Support
Wan, X., Jamaliding, Q., & Okamoto, T. (2009) · doi:10.1109/cise.2009.5363890 · Cited by 9 · cited 2× · see citing works
“, 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 · see citing works
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 · see citing works
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 · see citing works
RedTweet: recommendation engine for reddit
Nguyen, H., Richards, R., Chan, C.-C., & Liszka, K. J. (2016) · Journal of Intelligent Information Systems · doi:10.1007/s10844-016-0410-y · Cited by 8 · see citing works
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 · see citing works
Mining Intelligence and Knowledge Exploration
Prasath, R., Vuppala, A. K., & Kathirvalavakumar, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26832-3 · Cited by 8 · see citing works
Learning to Recommend Based on Slope One Strategy
Wang, Y., Yin, L., Cheng, B., & Yu, Y. H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29253-8_47 · Cited by 7 · cited 2× · see citing works
“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× · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Research of information recommendation system based on reading behavior
Li, Q., Dong, Z., & Li, T. (2008) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2008.4620666 · Cited by 6 · see citing works
Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
Kulkarni, S. (2012) · FedUni ResearchOnline (Federation University Australia) · doi:10.4018/978-1-4666-1833-6 · Cited by 6 · cited 1× · see citing works
“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 · see citing works
Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage
Musto, C., Narducci, F., Lops, P., de Gemmis, M., & Semeraro, G. M. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-15850-6_4 · Cited by 5 · see citing works
TISA: Topic Independence Scoring Algorithm
Martineau, J., Cheng, D. Y., & Finin, T. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39712-7_43 · Cited by 5 · see citing works
Identification of category associations using a multilabel classifier
Szymański, J., & Rzeniewicz, J. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.039 · Cited by 5 · see citing works
Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems
Hassan, M. T., Karim, A., Javed, F., et al. (2010) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2010.93 · Cited by 5 · see citing works
Constructing Topic Models of Internet of Things for Information Processing
Xin, J., Cui, Z., Zhang, S., He, T., Li, C., & Huang, H. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/675234 · Cited by 4 · cited 5× · see citing works
“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…”
Influential
A multi-institutional project-centric framework for teaching AI concepts
Russell, I., & Markov, Z. (2009) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2009.5350408 · Cited by 4 · cited 2× · see citing works
“Not sufficiently describing the approach [38] [51] [52] 3”
Background
An entertainment recommendation system using the dynamics of user behavior over time
Halder, S., Seddiqui, M. H., & Lee, Y.-K. (2014) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2014.7073094 · Cited by 4 · see citing works
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 · see citing works
Link Prediction Across Time via Cross-Temporal Locality Preserving Projections
Oyama, S., Hayashi, K., & Kashima, H. (2012) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e95.d.2664 · Cited by 3 · see citing works
Intelligent Techniques in Recommendation Systems
Dehuri, S., Patra, M. R., Misra, B. B., et al. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2542-6 · Cited by 3 · see citing works
Self-Driving Cars - An AI-Robotics Challenge.
Thrun, S. (2007) · The Florida AI Research Society · Cited by 2 · see citing works
The design and implementation of distributed mobile points of interest(POI) based on Mahout
Ma, L., Haihong, E., & Xu, K. (2011) · International Conference on Pervasive Computing and Applications (ICPCA) · doi:10.1109/icpca.2011.6106486 · Cited by 2 · cited 1× · see citing works
“Not sufficiently describing the approach [38] [51] [52] 3”
Background
Easy-to-explain feature synthesis approach for recommending entertainment video
Lee, T.-J., & Tseng, S. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.09.034 · Cited by 2 · see citing works
Software Engineering: The Trend.
Isazadeh, A. (2004) · Cited by 2 · see citing works
Combining User-Based and Item-Based Models for Collaborative Filtering using Stacked Regression
Liu, Q., Xiong, Y. M., & Huang, W. (2014) · Chinese Journal of Electronics · doi:10.23919/cje.2014.10851928 · Cited by 2 · see citing works
Feature enhanced time-aware recommender system
Karahodža, B., & Đonko, D. (2015) · International Conference on Information, Communication and Automation Technologies (ICAT) · doi:10.1109/icat.2015.7340527 · Cited by 2 · see citing works
Leveraging Semantic Web technologies for more relevant E-tourism Behavioral Retargeting
Lu, C., Stanković, M., & Laublet, P. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742001 · Cited by 2 · see citing works
Refine Social Relations and Differentiate the Same Friends’ Influence in Recommender System
Zhai, H., & Li, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26832-3_47 · Cited by 2 · see citing works
Lawyer information integration and recommendation by multi-source information validation
Liu, T., Fan, B., Hu, H., et al. (2011) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016956 · Cited by 1 · see citing works
An Online Bayesian Networks Model for E-Commercial Personalized Recommendation System
Zhang, S., Liu, L., & Dong, Y.-Z. (2007) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2007.4370754 · Cited by 1 · see citing works
Enhancing trustworthiness evaluation in internetware with similarity and non-negative constraints
Yan, G., Xu, F., Yao, Y., et al. (2013) · Asia-Pacific Symposium on Internetware · doi:10.1145/2532443.2532459 · Cited by 1 · cited 4× · see citing works
“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…”
BackgroundInfluential
Two 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 · see citing works
Catlinks - a category clustering algorithm based on multi-class regression
Liu, R., Ding, L. X., & Xie, L. (2014) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2014.7175752
Extension of similarity measures in VSM: From orthogonal coordinate system to affine coordinate system
Xuan, J., Lü, J., Zhang, G., et al. (2014) · doi:10.1109/ijcnn.2014.6889693 · cited 4×
“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…”
BackgroundInfluential
Digital Libraries
Agosti, 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: 38

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“…to make predictions and support decision-making tasks—are now powering many commonly used devices such as Web search engines (Richardson et al., 2006), social media applications (Hazelwood et al., 2018), online purchasing platforms (Portugal et al., 2018), and surveillance systems (Chokshi, 2019).”
Background
Designing Ground Truth and the Social Life of Labels
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“In most accounts of supervised (machine) learning, the ground truth is considered to be the “dependent variable” that is predicted by a collection of features (independent variables) [26, 58, 78].”
Background
RETRACTED: Health Recommendation System using Deep Learning-based Collaborative Filtering
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“The proposed method efficiently protects identity while maintaining the credibility of the information [20 – 25].”
Method
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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.”
Background
e-Recruitment recommender systems: a systematic review
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“It can also be found approaches that build ensembles, combining different techniques forming a bidirectional recommender system [40,72,84].”
Background
AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions
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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].”
Background
Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives
Alhijawi, B., Awajan, A., & Fraihat, S. (2022) · ACM Computing Surveys · doi:10.1145/3527449 · Cited by 63 · see citing works
“[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.”
Background
Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model
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An Iterative Stacking Method for Pipeline Defect Inversion With Complex MFL Signals
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“2) RMSE = ((1/N) ∑N 1 (yi − ŷi )(2))1/2, root mean square error [27], the smaller the better.”
BackgroundInfluential
Collaborative 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.”
Background
The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review
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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.”
BackgroundMethod
Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users
Dudekula, K. V., Syed, H., Basha, M. I. M., Ilango, S. S., Kasaraneni, P. P., Kumar, Y. V. P., Flah, A., & Azar, A. T. (2023) · Sustainability · doi:10.3390/su15032206 · Cited by 60 · see citing works
A systematic literature review of multicriteria recommender systems
Monti, D., Rizzo, G. F., & Morisio, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09851-4 · Cited by 60 · see citing works
IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness
Gyrard, A., & Sheth, A. (2020) · Smart Health · doi:10.1016/j.smhl.2019.100083 · Cited by 48 · see citing works
“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.”
Background
Link prediction in recommender systems based on vector similarity
Su, Z., Zheng, X., Ai, J., Shen, Y., & Zhang, X. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.125154 · Cited by 58 · see citing works
Review of application of artificial intelligence techniques in petroleum operations
Bahaloo, S., Mehrizadeh, M., & Najafi‐Marghmaleki, A. (2022) · Petroleum Research · doi:10.1016/j.ptlrs.2022.07.002 · Cited by 57 · see citing works
A Conceptual Framework on Defining Businesses Strategy for Artificial Intelligence
Caner, S., & Bhatti, F. (2020) · Contemporary Management Research · doi:10.7903/cmr.19970 · Cited by 63 · see citing works
“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).”
Background
A 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 · see citing works
“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].”
Background
Forgetting Practices in the Data Sciences
Muller, M. J., & Strohmayer, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517644 · Cited by 55 · see citing works
“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].”
Background
Providing effective recommendations in discussion groups using a new hybrid recommender system based on implicit ratings and semantic similarity
Riyahi, M., & Sohrabi, M. K. (2020) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2020.100938 · Cited by 54 · see citing works
Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study
Wang, Z., Zhao, X., Han, Z., Luo, L. C., Xiang, J., Zheng, S., Liu, G., Yu, M., Cui, Y., Shittu, S., et al. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116969 · Cited by 54 · see citing works
AI-powered in the digital age: Ensemble innovation personalizes the food recommendations
Yaiprasert, C., & Hidayanto, A. N. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100261 · Cited by 53 · see citing works
ML based sustainable precision agriculture: A future generation perspective
Priya, R., & Ramesh, D. (2020) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2020.100439 · Cited by 59 · see citing works
A Model of Social Explanations for a Conversational Movie Recommendation System
Pécune, F., Murali, S., Tsai, V., Matsuyama, Y., & Cassell, J. (2019) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3349537.3351899 · Cited by 53 · see citing works
“Over the last decades, large effort has been put into optimizing different algorithms to deliver the most accurate–or relevant–recommendations [24, 36].”
Background
The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability
Příhoda, D., Maritz, J. M., Klempíř, O., Džamba, D., Woelk, C. H., Hazuda, D. J., Bitton, D. A., & Hannigan, G. D. (2020) · Natural Product Reports · doi:10.1039/d0np00055h · Cited by 55 · see citing works
Prediction of carbon dioxide production from green waste composting and identification of critical factors using machine learning algorithms
Li, Y., Li, S., Sun, X., & Hao, D. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127587 · Cited by 51 · see citing works
On the preprocessing of physics-informed neural networks: How to better utilize data in fluid mechanics
Xu, S., Dai, Y., Yan, C. F., Sun, Z., Huang, R., Guo, D., & Yang, G. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.113837 · Cited by 51 · see citing works
“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 · see citing works
“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.”
Background
An 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 · see citing works
“We know that if we give more experiences (E) with a defined task (T) to a machine, its performance (P) improves [12].”
Background
Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda
Suraña‐Sánchez, C., & Aramendia-Muneta, M. E. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13027 · Cited by 67 · see citing works
A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model
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 · see citing works
Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias
Casillo, M., Gupta, B. B., Lombardi, M., Lorusso, A., Santaniello, D., & Valentino, C. (2022) · Electronics · doi:10.3390/electronics11071003 · Cited by 47 · see citing works
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 45 · see citing works
“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].”
Background
Application 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 45 · see citing works
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 · see citing works
Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing
Blomster, M., & Koivumäki, T. (2021) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00547-y · Cited by 44 · see citing works
Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective
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 · see citing works
“…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).”
Enhanced content-based fashion recommendation system through deep ensemble classifier with transfer learning
Suvarna, B., & Balakrishna, S. (2024) · Fashion and Textiles · doi:10.1186/s40691-024-00382-y · Cited by 42 · see citing works
“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).”
Method
An 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 · see citing works
“Further research work is going on to further enhance the capability of recommender systems in healthcare (Portugal, Alencar, and Cowan 2018).”
Background
Estimating 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 · see citing works
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 41 · see citing works
“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].”
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems
Pires, F., Leitão, P., Moreira, A. P., & Ahmad, B. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103884 · Cited by 39 · see citing works
Prediction of the jump height of transmission lines after ice-shedding based on XGBoost and Bayesian optimization
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 · see citing works
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 · see citing works
“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.”
Background
Towards 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 · see citing works
“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.”
Background
Predicting the depression in university students using stacking ensemble techniques over oversampling method
Vergaray, 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 · see citing works
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 38 · see citing works
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 · see citing works

Untersuchungen über die photosynthetische Leistung gelbblättriger Gehölze )
Michael, K. (1954) · Flora oder Allgemeine Botanische Zeitung · doi:10.1016/s0367-1615(17)33040-9 · Cited by 9 · see citing works
Systematic Study and Application of Machine Learning Algorithms in Recommender System Design
Sharma, S. G., & Sharma, D. (2018) · International Journal of Computer Sciences and Engineering · doi:10.26438/ijcse/v6i6.10211026
Exploratory Study of Machine Learning Algorithms in Recommender Systems
Momoh, F. O., Rakshit, S., & Vajjhala, N. R. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5207-3_48 · Cited by 4 · see citing works
Systematic Review on Machine Learning based Recommendation System
Abinaya, S., & Sharma, K. (2024) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit61487.2024.10580558 · Cited by 1 · see citing works
Tinjauan Penerapan Machine Learning pada Sistem Rekomendasi Menggunakan Model Klasifikasi
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Authors: Portugal, I., Alencar, P., & Cowan, D.
Affiliations: University of Waterloo (CA)
Corresponding author: Portugal, Ivens
ORCID: Portugal, Ivens
Alencar, Paulo
Cowan, Donald
Venue: Expert Systems with Applications
Publisher: Elsevier BV
Published: 2018-05
Volume: 97
Pages: 205-227
ISSN: 0957-4174 (ISSN-L)
DOI: 10.1016/j.eswa.2017.12.020
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 · 2017: 7
FWCI: 18.65
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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