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| Title | Reference |
|
| 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] |
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