Table 3: Selected primary studies.


Building accurate and practical recommender system algorithms using machine learning classifier and collaborative filtering[20]
DGA botnet detection using collaborative filtering and density-based clustering[21]
A multistage collaborative filtering method for fall detection[22]
Analysis and performance of collaborative filtering and classification algorithms[1]
Extracting a vocabulary of surprise by collaborative filtering mixture and analysis of feelings[4]
Content based filtering in online social network using inference algorithm[23]
Building switching hybrid recommender system using machine learning classifiers and collaborative filtering[8]
Imputation-boosted collaborative filtering using machine learning classifiers[24]
CRISP-an interruption management algorithm based on collaborative filtering[25]
A credit scoring model based on collaborative filtering[26]
Collaborative filtering recommender systems[2]
An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering[6]
Tweet modeling with LSTM recurrent neural networks for hashtag recommendation[27]
A two-stage cross-domain recommendation for cold start problem in cyber-physical systems[28]
ELM based imputation-boosted proactive recommender systems[29]
Twitter-user recommender system using tweets: a content-based approach[30]
A personalized time-bound activity recommendation system[31]
Automated content based short text classification for filtering undesired posts on Facebook[32]
Shilling attack detection in collaborative recommender systems using a meta learning strategy[33]
Building a distributed generic recommender using scalable data mining library[34]
Context-aware movie recommendation based on signal processing and machine learning[35]
Recommender systems using linear classifiers[36]
A survey of accuracy evaluation metrics of recommendation tasks[3]
Incorporating user control into recommender systems based on naive Bayesian classification[37]
Classification features for attack detection in collaborative recommender systems[38]
Automatic tag recommendation algorithms for social recommender systems[39]
Optimizing similar item recommendations in a semi-structured marketplace to maximize conversion[40]
Capturing knowledge of user preferences: ontologies in recommender systems[41]
Emotion-based music recommendation using supervised learning[42]
AWESOME—a data warehouse-based system for adaptive website recommendations[43]
Lexical and syntactic features selection for an adaptive reading recommendation system based on text complexity[5]
A smart-device news recommendation technology based on the user click behavior[44]
Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach[45]
A novel approach towards context based recommendations using support vector machine methodology[46]
A smartphone-based activity-aware system for music streaming recommendation[47]
An app usage recommender system: improving prediction accuracy for both warm and cold start users[48]
Proposing design recommendations for an intelligent recommender system logging stress[49]
A recommender system based on implicit feedback for selective dissemination of eBooks[50]
A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases[51]
An approach to content based recommender systems using decision list based classification with k-DNF rule set[52]
Probabilistic approach for QoS-aware recommender system for trustworthy web service selection[53]
Approach to cold-start problem in recommender systems in the context of web-based education[54]
Context and intention-awareness in POIs recommender systems[55]
A collaborative filtering-based re-ranking strategy for search in digital libraries[56]
Learning users’ interests by quality classification in market-based recommender systems[57]
Mobile content recommendation system for re-visiting user using content-based filtering and client-side user profile[58]
A hybrid collaborative filtering algorithm based on KNN and gradient boosting[59]
A scalable collaborative filtering algorithm based on localized preference[60]
Recommended or not recommended? Review classification through opinion extraction[61]
Meta-feature based data mining service selection and recommendation using machine learning models[62]
Personalized channel recommendation deep learning from a switch sequence[63]
Affective labeling in a content-based recommender system for images[64]
A novel approach towards context sensitive recommendations based on machine learning methodology[65]
A distance-based approach for action recommendation[66]
Ranking and classifying attractiveness of photos in folksonomies[67]
Consequences of variability in classifier performance estimates[68]
Machine learning and lexicon based methods for sentiment classification: a survey[9]
Machine learning algorithm selection for forecasting behavior of global institutional investors[69]
Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation[70]
Towards a method for automatically evolving Bayesian network classifiers[71]
A machine learning based trust evaluation framework for online social networks[72]
Automated problem identification: regression vs. classification via evolutionary deep networks[73]
Empirical evaluation of ranking prediction methods for gene expression data classification[74]
Inferring contextual preferences using deep auto-encoding[75]
Automatic recognition of text difficulty from consumers health information[76]
A hybrid approach for automatic model recommendation[77]
Learning instance greedily cloning naive Bayes for ranking[78]
Pairwise-ranking based collaborative recurrent neural networks for clinical event prediction[79]
Accurate multi-criteria decision making methodology for recommending machine learning algorithm[80]
A general extensible learning approach for multi-disease recommendations in a telehealth environment[81]
An efficient recommendation generation using relevant jaccard similarity[82]
An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction[83]
Automatic classification of high resolution land cover using a new data weighting procedure: the combination of k-means clustering algorithm and central tendency measures (KMC–CTM)[84]
Building a hospital referral expert system with a prediction and optimization-based decision support system algorithm[85]
Classification techniques on computerized systems to predict and/or to detect apnea: a systematic review[86]
Identification of category associations using a multilabel classifier[87]
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems[88]
S3Mining: a model-driven engineering approach for supporting novice data miners in selecting suitable classifiers[89]
The use of machine learning algorithms in recommender systems: a systematic review[11]