User Trust Evaluation in Social Review Platforms

This paper presents the standard model for evaluating user trustworthiness in social review systems that is based on machine learning. The suggested model analyzes user activity patterns, content reviews, and social interactions using algorithms like Support Vector Machines (SVM), Random Forest, and Gradient Boosting Machines (GBM). Model performance is improved by extracting pertinent attributes using feature engineering techniques. The framework's objectives are to spot dishonest people and protect online review sites' credibility. The findings show that the model efficiently evaluates user trustworthiness, offering a useful instrument for preserving the dependability of social review systems.