Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques

Abstract

This research explores the integration of multiple data mining approaches to improve recommendation accuracy in modern recommender systems. Despite significant advancements in recommendation algorithms, challenges persist in addressing the cold-start problem, data sparsity, and preference volatility. This study investigates how hybrid techniques combining collaborative filtering, content-based filtering, and knowledge-based approaches can overcome these limitations. Using a comprehensive dataset from an e-commerce platform with 2.3 million user-item interactions, we implemented a novel hybrid framework that dynamically switches between recommendation strategies based on contextual factors. Results demonstrate that our hybrid approach achieves a 27.4% improvement in recommendation accuracy compared to single-method approaches, with particularly strong performance in cold-start scenarios (41.2% improvement). The findings contribute to recommender systems theory by establishing an adaptive framework that optimizes recommendation strategies based on real-time data characteristics and user behavior patterns. This research has significant implications for e-commerce platforms, digital content providers, and social networks seeking to enhance user experience through more accurate and contextually relevant recommendations.