What question did this study set out to answer?

To explore AI-driven techniques for analyzing consumer behavior and integrating them into recommendation systems.

April 19, 2026JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT0 citationsOpen Access

AIBased Consumer Behaviour Analysis for Intelligent Recommendation Systems

Key Points

To explore AI-driven techniques for analyzing consumer behavior and integrating them into recommendation systems.
Reviewed state-of-the-art methodologies in consumer behavior analysis
Examined collaborative filtering, content-based filtering, and hybrid models
Included deep learning approaches like neural collaborative filtering and reinforcement learning
Conducted experimental comparisons of hybrid AI models against traditional methods
Hybrid AI models showed improved accuracy and scalability compared to traditional methods
Demonstrated enhanced personalization in recommendations
Identified challenges like data sparsity and cold-start problems in AI systems

Abstract

The rapid growth of digital platforms has led to an explosion of user-generated data, making consumer behavior analysis a critical component in modern business strategies. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significantly improved the ability to model, predict, and influence consumer decisions. This paper explores AI-driven techniques for analyzing consumer behavior and their integration into intelligent recommendation systems. It reviews state-of-the-art methodologies, including collaborative filtering, content-based filtering, and hybrid models, along with deep learning approaches such as neural collaborative filtering and reinforcement learning. Experimental comparisons demonstrate that hybrid AI models outperform traditional methods in terms of accuracy, scalability, and personalization. Challenges such as data sparsity, cold-start problems, and privacy concerns are also discussed, along with future research directions.

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