A Credibility-weighted Framework For Robust Recommendation, Rating Aggregation, And Community Trust In Movie Platforms

Abstract

Recommender Systems Often Reinforce Preference Homogeneity And Remain Vulnerable To Rating Manipulation. This Paper Proposes A Credibility-weighted Framework Integrating Review Usefulness Signals Into (1) Thematic Recommendation Modeling, (2) Weighted Rating Aggregation, And (3) Influence-based Community Ranking. Unlike Conventional Systems That Treat All User Interactions Equally, The Proposed Method Weights Contributions Based On Credibility Derived From Community Feedback. Experimental Evaluation On Benchmark Datasets Demonstrates Improved Diversity And Robustness While Maintaining Competitive Ranking Accuracy.

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