基于店铺忠诚度和店铺关联性的朴素贝叶斯推荐算法
Naive Bayesian Recommendation Algorithm Based on Store Loyalty and Store Relevance
DOI: 10.12677/CSA.2022.1211258, PDF,    科研立项经费支持
作者: 刘兴林, 黄 荣：五邑大学智能制造学部，广东 江门
关键词: 店铺忠诚度；店铺关联性；K-Means算法；朴素贝叶斯算法；Store Loyalty； Store Relevance； K-Means Algorithm； Naive Bayesian Algorithm
摘要: 本文将k-means算法、关联规则和朴素贝叶斯算法结合，提出一种基于店铺忠诚度和店铺关联性的朴素贝叶斯推荐算法。该算法首先使用k-means算法对用户进行店铺忠诚度聚类，再使用关联规则算法和用户的历史支付信息推测出店铺关联性，最后，使用朴素贝叶斯算法对用户进行训练，并利用训练结果对用户进行店铺预测。本文采用天池大数据提供的十五个月支付宝支付日志和浏览日志对该算法进行测试，并验证其可行性。
Abstract: Combining k-means algorithm, association rule and naive Bayesian algorithm, this paper proposes a naive Bayesian recommendation algorithm based on store loyalty and store relevance. First, the k-means algorithm is used to cluster the store loyalty. Second, the association rule algorithm and the user’s historical payment information are used to infer the store relevance. Finally, the naive Bayesian algorithm is used to train the user, and the training result is used to predict the store. In this paper, the fifteen-month Alipay payment logs and browsing logs provided by Tianchi Data were used to test the algorithm and verify its feasibility.
文章引用：刘兴林, 黄荣. 基于店铺忠诚度和店铺关联性的朴素贝叶斯推荐算法[J]. 计算机科学与应用, 2022, 12(11): 2526-2532. https://doi.org/10.12677/CSA.2022.1211258

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