›› 2015,Vol. 30 ›› Issue (6): 1188-1200.doi: 10.1007/s11390-015-1593-3

所属专题: Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

从兴趣到位置:微博系统中基于近邻关系的朋友推荐

Jin-Qi Zhu1(朱金奇), Li Lu2(鲁力), Member, CCF, ACM, IEEE, Chun-Mei Ma2(马春梅)   

  1. 1 天津师范大学计算机与信息工程学院, 天津 300387;
    2 电子科技大学计算机科学与工程学院, 四川成都 611731
  • 收稿日期:2015-05-18 修回日期:2015-08-22 出版日期:2015-11-05 发布日期:2015-11-05
  • 作者简介:Jin-Qi Zhu received her Ph.D. degree in computer science from University of Electronic Science and Technology of China, Chengdu, in 2009. She is currently an associate professor in the School of Computer and Information Engineering at Tianjin Normal University, Tianjin. Her research interests include parallel and distributed computing, mobile and wireless computing, wireless sensor networks, and vehicular ad hoc networks.
  • 基金资助:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61103227, 61172185, 61272526, 61472068, and 61173171, the China Postdoctoral Science Foundation under Grant No. 2014M550466, the Introduced Research Funds for Tianjin Normal University under Grant No. 5RL133, and the Tianjin Research Program of Application Foundation and Advanced Technology under Grant No. 15JCQNJC01400.

From Interest to Location: Neighbor-Based Friend Recommendation in Social Media

Jin-Qi Zhu1(朱金奇), Li Lu2(鲁力), Member, CCF, ACM, IEEE, Chun-Mei Ma2(马春梅)   

  1. 1 School of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China;
    2 School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731, China
  • Received:2015-05-18 Revised:2015-08-22 Online:2015-11-05 Published:2015-11-05
  • About author:Jin-Qi Zhu received her Ph.D. degree in computer science from University of Electronic Science and Technology of China, Chengdu, in 2009. She is currently an associate professor in the School of Computer and Information Engineering at Tianjin Normal University, Tianjin. Her research interests include parallel and distributed computing, mobile and wireless computing, wireless sensor networks, and vehicular ad hoc networks.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61103227, 61172185, 61272526, 61472068, and 61173171, the China Postdoctoral Science Foundation under Grant No. 2014M550466, the Introduced Research Funds for Tianjin Normal University under Grant No. 5RL133, and the Tianjin Research Program of Application Foundation and Advanced Technology under Grant No. 15JCQNJC01400.

近几年网络社会媒体蓬勃发展并吸引了大量Internet用户, 用户所发海量微博内容(tweets)成为理解社会媒体用户行为的重要资源。目前大量在线朋友推荐研究正是通过对微博内容进行分析从而得出用户的兴趣和喜好, 并把兴趣相似的用户彼此推荐, 以便获得用户感兴趣的信息资源。然而, 大多数这些现有研究忽略了社会媒体用户位置和兴趣之间潜在的紧密关系, 并不能使用户获得真正想要的信息资源。为此, 本文提出了基于近邻的朋友推荐(neighbor based friend recommendation, NBFR)思想, 通过把用户周围兴趣爱好相似的其他微博用户推荐给该用户, 为社交媒体用户提供了与周围可能感兴趣的人进行联系的独特渠道。此外, 由于位于同一地理位置的用户在线下相遇的可能性较大, NBFR也架起了用户线上和线下相联系的桥梁。本文思想分为两部分: 首先, 根据用户所发微博来挖掘用户的兴趣;其次, 为了进行精准兴趣推荐, 我们采取超立方体的方法来描述不同用户的不同兴趣主题。同时, 提出主题匹配的捷径算法(topic matching shortcut algorithm)进一步提高推荐的准确性。通过真实用户收集的数据证明: 与传统的推荐方法相比, NBFR具有较优越的推荐性能。

Abstract: Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The tweets these users posted provide an effective way of understanding user behaviors. A large amount of previous work benefits from mining user interest to make friend recommendation. However, the potentially strong but inconspicuous relation between location and interest interaction among social media users is overlooked in these studies. Different from the previous researches, we propose a new concept named neighbor-based friend recommendation (NBFR) to improve the friend recommendation results. By recommending surrounding users who have similar interest to each other, social media users are provided a unique opportunity to interact with surrounding people they may want to know. Based on this concept, we first mine users' interest from short tweets, and then propose to model the user interest with multiple topics under the hypercube structure for friend recommendation. At the same time, we also offer a topic matching shortcut algorithm for more extensive recommendation. The evaluations using the data gathered from the real users demonstrate the advantage of NBFR compared with the traditional recommendation approaches.

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