基于改进的协同过滤推荐算法在精准营销中的应用研究

Application of the Improved Collaborative Filtering Recommendation Algorithm in Commercial Precision Marketing

由于现在互联网的快速发展,各行各业面临着巨大的挑战。如何在互联网的巨大冲击下还能吸引更多的客户是企业最需要考虑的问题。本文主要讨论在大数据的大环境下,银行业是如何利用各种手段收集数据,建立自己的用户画像从而调整自己的营销策略。本文提供了一个改进的协同过滤算法——与时间相关的最近兴趣相似度计算方法,通过这个改进的协同过滤算法可以给银行的精准营销提供一个新的参考。

Nowadays with the rapid development of the Internet, all walks of life are facing great challenges. How to attract more customers under the huge impact of the Internet is the most important issue for enterprises to consider. This paper mainly discusses how the banking industry can use various means to collect data and build its own customer portraits to adjust its marketing strategy in the big data environment. This article provides an improved collaborative filtering algorithm--time-related similarity calculation method. This improved collaborative filtering algorithm can provide a new reference for the bank’s precision marketing.