北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2015, Vol. 38 ›› Issue (5): 118-124.doi: 10.13190/j.jbupt.2015.05.023

• 研究报告 • 上一篇    

位置社交网络的个性化位置推荐

徐雅斌1,2, 孙晓晨1   

  1. 1. 北京信息科技大学 计算机学院, 北京 100101;
    2. 北京信息科技大学 网络文化与数字传播北京市重点实验室, 北京 100101
  • 收稿日期:2015-01-08 出版日期:2015-10-28 发布日期:2015-10-28
  • 作者简介:徐雅斌(1962—),男,教授,E-mail:xyb@bistu.edu.cn.
  • 基金资助:

    国家自然科学基金项目(61370139);网络文化与数字传播北京市重点实验室项目(ICDD201506);北京市属高等学校创新团队建设与教师职业发展计划项目(IDHT20130519)

Individual Location Recommendation for Location-Based Social Network

XU Ya-bin1,2, SUN Xiao-chen1   

  1. 1. School of Computer, Beijing Information Science and Technology University, Beijing 100101, China;
    2. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing 100101, China
  • Received:2015-01-08 Online:2015-10-28 Published:2015-10-28

摘要:

为了有效改善位置社交网络的用户体验,提出了一种个性化位置推荐服务模型.综合考虑了用户的签到行为特点、用户特征及位置兴趣点的语义特征,并将蚁群算法与改进的混合协同过滤算法有效结合起来进行个性化位置推荐,以此提高个性化位置推荐的质量和效率.实验结果表明,提出的位置推荐模型的召回率、准确率和平均绝对误差值都明显优于已有方法.

关键词: 位置社交网络, 个性化位置推荐, 位置服务, 协同过滤算法

Abstract:

In order to effectively improve the users' experience for location social networks, a model of personalized location recommendation service was proposed. Considering the users' check-in behavior features, the users' characteristics and semantic features of interested location point, this model combines the ant colony algorithm with the improved hybrid collaborative filtering algorithm to improve the quality and efficiency of the individual location recommendation. Experiments show that, the recall, accuracy and average absolute error value of the location recommendation model proposed in this article is superior to the existing methods.

Key words: location-based social network, individual location recommendation, location-based service, collaborative filtering

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