摘要
为了解决线下消费信息获取不全面、不便捷问题,让消费者在线下消费时享受到与线上消费相似的推荐服务,经可行性调研,采用基于流行度的算法、基于用户的协同过滤算法、基于项目的协同过滤算法,并对算法进行优化,开发以推荐服务为主、位置服务为辅的商场推荐系统,帮助用户解决线下购物遇到的选择问题。主要实现了地图定位功能、搜索商品商店功能、商品商店推荐功能、商品商店评分功能、收藏商品商店功能。
In order to solve the problem that offline consumption information is not comprehensive and convenient,and allow consumers to enjoy similar recommendation services to online consumption when they consume offline,Through feasibility study,the popularity-based algorithms,user-based collaborative filtering algorithms,and project-based collaborative filtering algorithms are optimized,and a shopping mall recommendation system based on recommended services and supplemented by location services is developed to help users solve the choice problems encountered in offline shopping.The shopping mall recommendation system mainly implements the map positioning function,the search commodity shop function,the commodity shop recommendation function,the commodity shop rating function,and the collection commodity shop function.
作者
刘洋
高文超
LIU Yang;GAO Wenchao(China University of Mining and Technology<Beijing>,Beijing 100083,China)
出处
《天津科技》
2019年第8期46-47,51,共3页
Tianjin Science & Technology
关键词
商场
推荐系统
个性化
shopping mall
recommendation system
personalization
