Think Buy: A Scalable, Context-Rich AI Model for Personalized E-Commerce Recommendations
DOI:
https://doi.org/10.70153/IJCMI/2024.16304Keywords:
AI-based recommendations, personalized systems, e-commerce, collaborative filtering, Graph Neural NetworksAbstract
Personalized recommendation systems are critical components in large-scale e-commerce ecosystems, where user engagement and conversion depend heavily on the system’s ability to adapt to diverse behavioral patterns and contextual factors. Conventional approaches, including collaborative filtering and rule-based heuristics, often exhibit limitations in capturing complex user-item relationships, suffer from cold-start issues, and lack responsiveness to temporal context. This paper presents a novel AI-driven hybrid recommendation framework that integrates graph-based relational modeling and deep contextual sequence learning to enhance recommendation accuracy, robustness, and scalability.
The proposed architecture leverages Graph Neural Networks (GNNs) to learn latent representations from the user-item bipartite interaction graph, capturing higher-order collaborative signals. In parallel, a Transformer-based encoder processes sequential user interactions enriched with contextual metadata such as timestamp, device type, and location, enabling temporal and situational awareness. A fusion mechanism combines the outputs of both modules to compute relevance scores, which are further refined using a real-time feedback loop incorporating click-through and purchase logs.
Empirical evaluation is conducted on two benchmark datasets—Amazon Electronics and Movielens-1M—using standard metrics including Precision@10, Recall@10, NDCG@10, and item coverage. The proposed model achieves state-of-the-art performance, outperforming competitive baselines such as NCF, LightGCN, and BERT4Rec, with notable gains in both accuracy and recommendation diversity. Furthermore, the system demonstrates a 17.5% improvement in cold-start scenarios, validating its effectiveness in addressing key limitations of existing models.
This research contributes a scalable, context-aware, and dynamically adaptive recommendation paradigm, offering practical implications for next-generation personalized product discovery in intelligent e-commerce platforms.
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