作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程

• 开发研究与工程应用 • 上一篇    下一篇

基于多模式的室内兴趣点推荐系统设计与实现

冯锦海1,杨连贺1,蒋鑫龙2   

  1. (1.天津工业大学计算机科学与软件学院,天津 300387; 2.中国科学院计算技术研究所,北京 100190)
  • 收稿日期:2014-08-18 出版日期:2015-08-15 发布日期:2015-08-15
  • 作者简介:冯锦海(1989-),男,硕士研究生,主研方向:无线定位,机器学习,数据挖掘;杨连贺,教授、博士生导师;蒋鑫龙,博士研究生。
  • 基金资助:
    国家自然科学基金资助项目(61173066,41201410);广东省战略性新兴产业发展专项基金资助项目(2011912030)。

Design and Implementation of Indoor Point of Interest Recommended System Based on Multi-mode

FENG Jinhai  1,YANG Lianhe  1,JIANG Xinlong  2   

  1. (1.School of Computer Science & Software Engineering,Tianjin Polytechnic University,Tianjin 300387,China; 2.Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)
  • Received:2014-08-18 Online:2015-08-15 Published:2015-08-15

摘要: 现有基于无线局域网(WLAN)的定位方法能为用户提供相对精确的室内位置信息,但不能有效挖掘并利用用户移动轨迹隐藏的信息。为此,提出一种基于室内用户移动轨迹聚类的推荐算法,结合WLAN定位技术从用户移动轨迹中提取停留点,使用DBSCAN算法对停留点进行聚类分 析,发现用户兴趣点并提取特性,采用决策树算法对用户进行分类,从而实现用户个性化推荐服务。基于微信平台设计并实现的室内兴趣点推荐系统验证了该算法的有效性,可为用户提供基于内容的商品推荐与个性化店铺推荐服务。

关键词: 兴趣点, 停留点, 轨迹聚类, 无线定位, 推荐系统

Abstract: Based on Wireless Local Area Network(WLAN),positioning methods can provide a relatively accurate indoor positioning information,but it can not effectively utilize the hidden information of user trajectory.This paper presents an recommended algorithm based on indoor user trajectory clustering,combines with WLAN positioning technology to extract Point of Interest(POI) from a user trajectory,uses DBSCAN algorithm to find and extract user POI feature,and employs Decision Tree(DT) algorithm to realize user classification and personalization recommendation service.Indoor POI recommendation system based on weChat platform is designed and implemented to verify the validity of proposed algorithm,and it can provide personalized recommended and recommended service based on content for users.

Key words: Point of Interest(POI), point of arrest, trajectory clustering, wireless positioning, recommended system

中图分类号: