Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Hassan, Taha; Edmison, Bob; Stelter, Timothy L.; McCrickard, D. Scott

doi:10.1145/3450613.3456811

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The link prediction for the cold start recommendation system is proposed to boost the performance of existing recommendation systems [45]. Social trust is an essential factor to be considered in the recommendation systems [46]. The pairwise trust prediction via matrix fac-torization is propsoed by [47], to the intensity of the trust level in social networks.…”

Section: A Features Based Movie Quality Prediction Techniquesmentioning

confidence: 99%

Prediction of Movie Quality via Adaptive Voting Classifier

et al. 2022

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Information retrieval from huge social web data is a challenging task for conventional search engines. Recently, information filtering recommender systems may help to find movies, however, their services are limited because of not considering movie quality aspects in detail. Prediction of movies can be improved by using the characteristics of social web content about a movie such as social-quality, tag quality, and a temporal aspect. In this paper, we have proposed to utilize several features of social quality, user reputation and temporal features to predict popular or highly rated movies. Moreover, enhanced optimization-based voting classifier is proposed to improve the performance on proposed features. Voting classifier uses the knowledge of all the candidate classifiers but ignores the performance of the model on different classes. In the proposed model, weight is assigned to each model based on its performance for each class. For the optimal selection of weights for the candidate classifiers, Genetic Algorithm is used and the proposed model is called Genetic Algorithm Voting (GA-V) classifier. After labeling the suggested features by using a fixed threshold, several classifiers like Bayesian logistic regression, Naïve Bayes, BayesNet, Random Forest, SVM, Decision Tree, LSTM and AdaboostM1 are trained on MovieLens dataset to find high-quality/popular movies in different categories. All the traditional ML models are compared with GA-V in terms of precision, recall and F1 score. The results show the significance of the proposed features and proposed GA-V classifier.

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Section: A Features Based Movie Quality Prediction Techniquesmentioning

confidence: 99%