摘要
探讨在互联网电子商务的飞速发展的趋势下,通过推荐算法为用户提供精而准的购物体验。推荐算法是通过分析大量样本用户的决策行为,加以严密的数学论证分析,推测出用户可能的决策行为。电子商务网站使用推荐算法能够极大地提高用户粘性和增加企业收益。对基于用户的协同过滤算法进行剖析,指出利用有效的推荐算法可以明显提高数据管理能力。
Discusses the recommendation algorithm to provide users with better shopping experience.The recommendation algorithm analyzes the decision-making behavior of a large number of sample users,and make a rigorous mathematical demonstration and analysis to speculate the possible decision-making behavior of users.E-commerce website using recommendation algorithm can greatly improve user stickiness and increase enterprise revenue.Analyzes the collaborative filtering algorithm based on users,and points out that the effective recommendation algorithm can significantly improve the data management ability.
作者
吴建帆
曾昭平
郑亮
李琥
管孜恒
徐寅
WU Jian-fan;ZENG Zhao-ping;ZHENG Liang;LI Hu;GUAN Zi-heng;XU Yin(School of Information Management,Shanghai Lixin University of Accounting and Finance,Shanghai 201209)
出处
《现代计算机》
2020年第19期27-29,67,共4页
Modern Computer
基金
上海立信会计金融学院2019年度一流本科建设项目(No.B1-12-7101-18-003Z)。
关键词
基于用户
协同过滤
推荐算法
相似度
User Based
Collaborative Filtering
Recommendation Algorithm
Similarity
