基于个性化推荐算法的电子商务系统实践研究

Practical Research on E-Commerce System Based on Personalized Recommendation Algorithm

摘要: 研究重点放在个性化推荐算法于电子商务范围的应用考察之上，把主流推荐技术做系统归纳之后，针对传统基于用户的协同过滤模型提出革新策略，在融合用户静态属性以及动态兴趣衰减机制的基础上，结合Redis缓存架构来改良计算效率，凭借创建起模拟中小型电商平台环境的实验场景展开实证分析，比较起经典方法，改良过的算法不仅提高响应速度，而且使推荐精准度得到改善，于是推动点击转化率，复购频率以及平台总交易额(GMV)等重要经营指标得到明显优化，这便证实了所提方案在实际商业环境里具备可行性和一定价值。

Abstract: The research focuses on the application of personalized recommendation algorithms in the field of e-commerce. After systematically summarizing mainstream recommendation technologies, innovative strategies are proposed for traditional user based collaborative filtering models. Based on the integration of user static attributes and dynamic interest decay mechanisms, Redis caching architecture is used to improve computational efficiency. By creating experimental scenarios that simulate small and medium-sized e-commerce platform environments, empirical analysis is conducted. Compared with classical methods, the improved algorithm not only improves response speed, but also improves recommendation accuracy, thereby promoting significant optimization of important business indicators such as click through conversion rate, repurchase frequency, and platform total transaction volume (GMV). This confirms the feasibility and certain value of the proposed solution in practical business environments.