电商推荐系统中查询优化研究  

Research on query optimization in E-commerce recommendation system

作　　者：张玉莹 宁士勇[1] ZHANG Yu-ying;NING Shi-yong(School of Computer and Information Engineering,Harbin University of Commerce,Harbin 150028,China)

机构地区：[1]哈尔滨商业大学计算机与信息工程学院,哈尔滨150028

出　　处：《哈尔滨商业大学学报（自然科学版）》2019年第2期199-203,共5页Journal of Harbin University of Commerce：Natural Sciences Edition

摘　　要：分析了电商推荐系统实现过程中的瓶颈问题,对比目前经常采用的查询优化技术,通过结合SSM+Duboox框架,尝试采用数据缓存、分面查询和Solr搜素等方法来进行查询优化,提升查询速度;对电商推荐系统进行业务逻辑分析,设计出三级商品分类列表查询功能,方便用户查询,提高了推荐的精准度.This paper analyzed the bottleneck problem in the implementation process of e-commerce recommendation system.Compared with the query optimization technology that was often used at present,this paper used the data cache,face-to-face query and Solr search method to query by SSM+Duboox framework.Optimization to improve query speed;this paper analyzed the business logic of the E-commerce recommendation system,and designed a three-level commodity classification list query function,which was convenient for users to query and improve the accuracy of recommendation.

关 键 词：电子商务 查询优化 分面搜索 缓存 SSM框架 Solr搜索技术 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]