IFP-DeepFM: Integrating Improved FP-Growth and Attention-Based Deep Factorization Machines for User Purchase Behavior Modeling and E-commerce Recommendation - Li, Ning; Gu, Lihan (Informatica, 2026)
This study examines how intelligent recommendation algorithms can be used to analyze user purchase behavior on e-commerce platforms and to support the development of more effective marketing strategies. First, a systematic review of existing recommendation algorithms is conducted, which shows that traditional methods have limitations when processing large-scale and complex user data. To address these issues, an Improved Frequent Pattern-growth (IFP-growth) algorithm is introduced. By incorporating time-window parameters, the algorithm mines frequent patterns from user behavior logs and extracts timely and relevant purchasing features. In parallel, an Attention-enhanced Deep Factorization Machine (DeepFM) is adopted, integrating the strengths of Factorization Machines and Deep Neural Networks. The attention mechanism further refines feature-interaction representations, enabling more accurate and personalized recommendations. To validate the effectiveness and robustness of the proposed model, experiments are conducted using real behavioral data from a major e-commerce platform over a threemonth period, comprising approximately 1.2 million behavior logs, 80,000 active users, and 100,000 products. The IFP-DeepFM model is compared with traditional collaborative filtering, IFP-growth, and standard DeepFM algorithms. The results show that IFP-DeepFM outperforms DeepFM in precision, recall, F1-score, and the area under the precision–recall curve by 6.81%, 11.9%, 9.51%, and 11.5%, respectively. In practical metrics such as Click-Through Rate (CTR) and Conversion Rate (CVR), the model also achieves improvements of 10.4% and 8.9%. Based on these results, this study outlines several marketing strategy implications for e-commerce platforms, including personalized recommendations, targeted advertising, and user behavior prediction. These strategies enhance user satisfaction and platform performance while providing data-driven support for operational decision-making. The proposed IFP-DeepFM model demonstrates a practical approach to analyzing user purchase behavior and refining marketing strategies in large-scale e-commerce settings.