Research on the Design and Implementation of Preschool Education Book Recommendation System Based on Data Mining Algorithm

This study improves frequent item set mining efficiency by introducing UFIM, a modified FP-Growth algorithm using the UFP-tree. The one-way frequent pattern tree uses a non-recursive method to check if an endpoint’s support count meets the minimum threshold. If not, the constrained subtree yields no frequent item sets; otherwise, the set includes nodes excluding the root. Experimental results show UFIM processes faster than similar algorithms, with a peak signal-to-noise ratio (PSNR) of 27.0 to 27.6 and structural similarity fluctuating between 0.86 and 0.92. To enhance UFIM’s performance in big data environments, a parallelization strategy was implemented on the Spark platform. Frequent 1-item sets are identified in parallel, and data for subtrees are distributed across multiple nodes. Each node mines item sets independently, aggregating local results into a global frequent set. The parallelized UFIM algorithm, demonstrated through a book recommendation system, efficiently analyzes user purchase history to suggest accurate book recommendations.