Integrating Granular Pattern Discovery With Collaborative Ranking

Online marketplaces offer vast product assortments, presenting consumers with the contradiction of choice, where abundant options delay the discovery of ideal items. While recommendation systems dynamically assist users, they struggle to address changing preferences and challenges such as data sparsity and the cold-start problem. This research proposes an innovative hybrid framework merging granular pattern discovery (GPD) with collaborative filtering (CF) to boost recommendation accuracy. The GPD stage applies techniques including decision trees, sequence mining, data transformations, clustering, and text analytics to heterogeneous user activity logs, extracting detailed behavioral patterns. These granular patterns enrich user profiles, enabling CF to more effectively match users by reducing data sparsity and enhancing prediction accuracy for new users or products with limited historical data. A graph-based algorithm matches consumers with associated preferences through similarity computations between profile vectors, seamlessly integrating neighborhood-based collaboration for personalized product rating predictions. Top-N recommendations are generated by ranking items based on predicted user–item affinity. Extensive testing demonstrates significant improvements over existing methods in key accuracy and ranking metrics, achieving a precision of 0.89 (compared with baseline methods of 0.68 and 0.62), a recall of 0.85, and an F1-score of 0.87. These improvements directly enhance user experience by increasing recommendation relevance and item discoverability, crucial for engagement and conversion in practical e-commerce scenarios. The proposed fusion approach significantly improves adaptability and accuracy, setting the stage for next-generation intelligent recommendation engines responsive to evolving user interests

Adaptability · Analytics · Collaborative filtering · Recommender system · Customer churn and segmentation · Recommender Systems and Techniques · Sentiment Analysis and Opinion Mining

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