基于大数据多因素关联规则的在线商品推荐系统优化研究

Research on Optimization of Online Product Recommender System Based on Big Data Multi-factor Association Rules

文中聚焦于大数据背景下的在线商品推荐系统,通过数据挖掘与关联规则分析名创优品淘宝店的商品数据,揭示了商品价格区间和相似商品类别之间的关联性。应用Apriori算法,发现消费者对不同价格区间的商品存在明显的偏好,并倾向于购买相似类别的商品。这些发现为电商平台提供了精准推荐策略,有助于提高推荐系统的准确性和效率,提升消费者满意度和企业经济效益。

This paper focuses on the online product recommender system under the background of big data,analyzes the product data of Mingchuang Youpin Taobao store through data mining and association rules,and reveals the correlation between product price ranges and similar product categories.Applying Apriori algorithm,it is found that consumers have obvious preferences for products in different price ranges and tend to buy products of similar categories.These findings provide precise recommendation strategies for e-commerce platforms,which help to improve the accuracy and efficiency of recommender systems,improve consumer satisfaction and corporate economic benefits.