基于机器学习的电子商务个性化推荐服务及大数据挖掘研究

Zhi Zeng, Hui-ke Rao, Ai-ping Liu
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引用次数: 15

摘要

机器学习(ML)是解决大数据挖掘问题的主要方法之一。机器学习可以通过积累先验知识,使电子商务系统自我创新和改进。电子商务企业通过交易、互动、观察产生的大数据,可以极大地为营销策略提供决策服务。本文以茶器企业的电子商务数据为例,利用FP-grow算法得到频繁项集,进而挖掘和分析用户行为的关联规则,得到特征向量作为用户分类的基础,然后在特征向量上使用朴素贝叶斯算法实现聚类学习,用于精准营销和个性化在线推荐服务。最后,我们通过商品销售产生的利润来评估ML进行大数据挖掘的可行性。实验结果表明,ML不仅可以大大提高大数据挖掘的性能,还可以实现精准营销,并且可以进一步提高每种商品20%左右的边际利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on personalized referral service and big data mining for e-commerce with machine learning
Machine learning (ML) is one of the main methods to address the problem of big data mining. ML can enable the e-commerce system upon self-innovate and improvement by accumulating prior knowledge. The big data produced by transaction, interaction and observation from e-commerce enterprises can greatly provide decision-making service for marketing strategy. In this paper we take the e-commerce data of tea-device enterprise as an example, use the FP-grow algorithm to get the frequent item sets, so as to mining and analyze association rule of user behavior to get feature vector as the basis of user classification, then use Naive Bayesian algorithm on the feature vector to implement clustering learning for precision marketing and personalized online referral services. Finally, we evaluate the feasibility of big data mining with ML through the profit produced by the sale of goods. Experimental results show that ML can not only greatly improve the performance in big data mining, can also achieve precise marketing, and can further increasing about 20% marginal profit for each type of goods.
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