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中国科学院软件研究所
  
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俞菲,李治军,车楠,姜守旭.一种面向获取空间信息的潜在好友推荐算法.软件学报,2017,28(8):2148-2160
一种面向获取空间信息的潜在好友推荐算法
Potential Friend Recommendation Algorithm for Obtaining Spatial Information
投稿时间:2015-06-23  修订日期:2016-03-18
DOI:10.13328/j.cnki.jos.005118
中文关键词:  LBSN(location-based mobile social network)  朋友推荐  核密度估计  签到行为概率分布
英文关键词:LBSN (location-based mobile social network)  friend recommendation  kernel density estimation  check-in behavior probability distribution
基金项目:国家自然科学基金(61370214,61300210)
作者单位E-mail
俞菲 哈尔滨工业大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001  
李治军 哈尔滨工业大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001  
车楠 哈尔滨理工大学 软件学院, 黑龙江 哈尔滨 150001  
姜守旭 哈尔滨工业大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001 jsx@hit.edu.cn 
摘要点击次数: 1026
全文下载次数: 644
中文摘要:
      随着社交网络的不断发展,朋友推荐已成为各大社交网络青睐的对象,在能够帮助用户拓宽社交圈的同时,可以通过新朋友获取大量信息.由此,朋友推荐应该着眼于拓宽社交圈和获取信息.然而,传统的朋友推荐算法几乎没有考虑从获取信息的角度为用户推荐潜在好友,大多是依赖于用户在线的个人资料和共同的物理空间中的签到信息.而由于人们的活动具有空间局部性,被推荐的好友分布在用户了解的地理空间,并不能满足用户通过推荐的朋友获取更多理信息的需求.采用用户在物理世界中的签到行为代替虚拟社交网络中的用户资料,挖掘真实世界中用户之间签到行为的相似性,为用户推荐具有相似的签到行为且地理位置分布更广泛的陌生人,能够增加用户接受被推荐的陌生人成为朋友的可能性,在保证一定的推荐精度的基础上,增加用户的信息获取量.采用核密度估计估算用户签到行为的概率分布,用时间熵度量签到行为在时间上的集中程度,选择可以为用户带来更多新的地理信息的陌生人作为推荐的对象,通过大规模Foursquare的用户签到数据集,验证了该算法能够在精度上保证与目前已有的LBSN上陌生人推荐算法的相似性,在信息扩大程度上高于上述已有算法.
英文摘要:
      Along with the development of online social networks, friend recommendation becomes the favor of the major social networks. It can help people to meet new friends for expanding the scale of social network, which in turn allows people to receive more information from their friends. Therefore, friend recommendation should be focused on expanding the scale of social network and obtaining information form recommended friends. However, existing friend recommendation methods barely consider the people information need, and they are mainly based on the simple and limited user profiles, and are agnostic to users' offline behaviors in the real world. Because human activity in the physical world has a spatial locality, the recommended friends through the existing recommendation methods are limited in geographic space which the target user know. As a result, the recommendation cannot provide more new information on geography to meet the target's need on information. This paper first proposes a new friend recommendation method based on the offline check-in behaviors in the real world instead of the online user profiles, and mines check-in behavior similarity between users in the real world. The essential goal of friend recommendation is to provide users with more new information. In order to meet the requirements of user getting more geographical location information, the recommendation systems can recommend the strangers in broader check-in geography distribution for the target users. Meanwhile, when the recommended friends and the target users have the similar check-in behaviors, it is more probable for the users to accept recommended strangers. Kernel density estimation (KDE) is used to estimate each user's check-in behavior probability distribution and the time entropy to filtering some noise that have side effects on overall check-in behavior similarity, then the recommended strangers who can bring a wider range of new strangers geographic information for the target users are selected. Lastly, a large-scale user check-in data-set of Foursquare is used to validate recommendation precision and the degree of information expanding of this approach. The experimental results show that the proposed approach outperforms the existing friend recommendation methods on the aspect of the information expanding degree while maintaining the recommendation precision of the state-of-the-art stranger recommendation methods.
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