一种基于用户兴趣的位置服务推荐算法  被引量:4

A Recommendation Algorithm of Location Service Based on User Interest

在线阅读下载全文

作  者:邰淳亮 谢怡[1] 孙知信[1,2] 

机构地区:[1]南京邮电大学计算机学院,江苏南京210003 [2]南京邮电大学物联网学院,江苏南京210003

出  处:《计算机技术与发展》2017年第9期64-69,共6页Computer Technology and Development

基  金:国家自然科学基金资助项目(60973140;61170276;61373135);江苏省产学研项目(BY2013011);江苏省科技型企业创新基金项目(BC2013027);江苏省高校自然科学研究重大项目(12KJA520003)

摘  要:随着移动网络经济用户数、商户数和覆盖范围的扩大,基于用户位置的商家信息推送势必会经历"信息爆炸"和"信息过载",而解决因信息过载导致用户与商家之间的信息迷失的最有效办法是基于用户兴趣的用户位置服务(LBS)推荐。为此,在分别从位置服务技术、用户的兴趣模型,以及个性化推荐算法三个方面深入研究移动位置服务的个性化推荐系统、分析个性化推荐研究现状以及对比分析各种推荐算法的基础上,基于贝叶斯理论,提出了一种适用于移动位置服务的个性化推荐算法。该算法能准确地预测用户在某一情景下消费的兴趣偏好。为验证所提出算法的有效性和可行性,基于所构建的推荐系统进行了实验验证测试。实验结果表明,所提出的算法能够有效地向移动用户提供个性化推荐服务。With the increase of consumers and merchants, information pushing services based on consumers' localities are deemed to go through information explosion and communication overload. Locality service recommendation based on consumers' interests is an effec- tive method to deal with information misleading between consumers and merchants stemmed from the information overload. In order to solve the problem mentioned above, after the individual recommendation system based on mobile locality has been researched on three as- pects,locality service technique,consumers' interest model and individual recommendation algorithms respective|y as well as the status of investigation on individual recommendation and contrast analysis on various recommendation algorithms. Based on Bayesian theory, an in- dividual recommendation algorithm suitable for mobile location services is proposed, which can predict consumers' consumption interest accurately in a certain scenario. In order to verify its effectiveness and feasibility,a series of simulation experiments for verification have been conducted with the established recommendation system, which show that it has provided efficient individual recommendation services to mobile consumers.

关 键 词:位置服务 用户建模 个性化推荐 用户兴趣 

分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象