结合商圈位置区域模型的商品推荐算法

为解决电子商务冲击下线下销售萎靡及互联网"信息爆炸"下用户挑选商品时耗时耗力等问题,本文引入商圈位置区域模型,即基于圆形过滤方法与改进的基于分区的DBSCAN密度聚类算法对浙江省某一行业全域范围下25万商户订单数据进行地理位置特征分析,并结合时间衰减参数进行传统推荐算法改进,提出了面向商圈流行度的商品推荐算法与面向商圈相似度的协同过滤算法.实验结果表明,算法在推荐准确率上明显优于传统推荐算法,且一定程度上缓解了冷启动和推荐商品惊喜度不足的问题,有其实用价值与研究意义.

Commodity Recommendation Algoriehm Based on Location Area Model of Business-circle

In order to solve the problems of embarrassing sales under the impact of e-commerce and time consuming and exhausting effort of users when chosing the commodity under the "information explosion" of the Internet, this paper introduces the location model of the business-circle based on the circular filtering method and the improved partition-based DBSCAN density clustering algorithm for Zhejiang Province. The geographic location characteristics of the 250 000 merchants'' order data in a certain industry of Zhejiang Province are analyzed, and the traditional recommendation algorithm is improved by combining the time decay parameters. The commodity recommendation algorithm for the popularity of the business-circle and the collaborative filtering algorithm for the similarity of the business-circle are proposed. The experimental results show that the algorithm is superior to the traditional recommendation algorithm in terms of recommendation accuracy rate, and to some extent, it alleviates the problem of insufficient cold start and recommended product surprise, which has its practical value and research significance.