Research on optimization of personalized recommendation method based on RFMQ model- taking outdoor sports products in...

With the rapid development of the global digital economy, cross-border e-commerce has rapidly emerged and developed at a high speed, and has become a crucial bridge connecting global markets. This research focuses on the cross-border e-commerce sector of outdoor sports products, in response to the common problems in the cross-border e-commerce field, such as "information overload" and "insufficient recommendation accuracy," a personalized recommendation optimization framework integrating customer value segmentation and collaborative filtering is proposed. Based on the classic RFM model, the purchase quantity indicator (Quantity) is introduced to construct the RFMQ model, thereby more comprehensively characterizing user behavior characteristics. Further, the customer value stratification is achieved by using the indicator segmentation method and the K-means clustering algorithm, and a differentiated collaborative filtering recommendation mechanism is designed based on the segmented groups. Through a five-fold cross-validation experiment, it is shown that the proposed method significantly outperforms the traditional collaborative filtering model in the TOPN recommendation task. Specifically, when the number of recommended products is between 3 and 7, the RFMQ recommendation model based on indicator segmentation performs best in terms of F1 score (for example, when TOPN = 5, the F1 value increases from 0.1709 to 0.3093), and the method based on K-means clustering also shows a stable improvement (with the F1 value reaching 0.267 at the same time). The results indicate that the indicator segmentation method has a significant advantage in smaller recommendation quantity scenarios. This study verifies the effectiveness of the RFMQ model in customer segmentation and recommendation performance optimization, providing an operational solution for e-commerce platforms to implement precise marketing, enhance user stickiness and commercial competitiveness, and is particularly suitable for low-cost and high-efficiency personalized recommendation scenarios of small and medium-sized enterprises.