个性化推荐算法在电子商务系统的实践探索

Practice and Exploration of Personalized Recommendation Algorithm in Computer E-commerce System

通过协同过滤、基于内容的推荐及混合推荐算法,文章改进了电子商务系统中的推荐系统。本研究通过收集百万条用户数据构建精准用户画像,并采用相似度计算生成个性化推荐。经过优化后系统准确率、召回率、F1分数均显著提升,点击与购买转化率也大幅增长,但个性化推荐仍面临数据稀疏、冷启动及多样性问题。针对这种情况,本文提出了数据填充、引入热门商品等策略,以平衡准确性与多样性。

Through collaborative filtering,content-based recommendations and hybrid recommendation algorithms,the paper improves the recommendation system in e-commerce system.This research builds accurate user profiles by collecting millions of user data,and uses similarity calculation to generate personalized recommendations.After optimization,the system’s accuracy,recall rate,and F1 score are all significantly improved,and the click and purchase conversion rates have also increased significantly.However,personalized recommendations still face problems of data sparseness,cold start,and diversity.In response to this kind of situation,this paper proposes strategies such as data filling and introducing popular goods to balance accuracy and diversity.