摘要
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.
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.
基金
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.
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.E-mail： jsjzhujinqi@mail.tjnu.edu.cnLi Lu received his Ph.D. degree in computer science from the State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing, in 2007. He is currently an associate professor in the School of Computer Sci- ence and Engineering in the University of Electronic Science and Technology of China, Chengdu. His research interests include parallel and distributed computing, RFID technology, and wireless network security. He is now a member of CCF, ACM, and IEEE.E-mail： luli2009@uestc.edu.cnChun-Mei Ma is now a Ph.D. stu- dent in the School of Computer Science and Engineering in the University of Electronic Science and Technology of China, Chengdu. Her research interests include parallel and distributed computing, mobile and wireless computing, wireless sensor networks, and vehicular ad hoc networks.E-mail： chunmeima2011@gmail.com
