Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques
Year: 2025
Type: article
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.
Author: Mini T V
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911922464
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