Abstract
With the advent and popularity of the social network, social graphs become essential to improve services and information relevance to users for many social media applications to predict follower/followee relationship, community membership, and so on. However, the social graphs could be hidden by users due to privacy concerns or kept by social media. Recently, connections discovered from user-shared images using machine-generated labels are proved to be more accessible alternatives to social graphs. But real-time discovery is difficult due to high complexity, and many applications are not possible. This article proposes an efficient computation framework for connection discovery using user-shared images, which is suitable for any image processing and computer vision techniques for connection discovery on the fly. The framework includes the architecture of online computation to facilitate real-time processing, offline computation for a complete processing, and online/offline communication. The proposed framework is implemented to demonstrate its effectiveness by speeding up connection discovery through user-shared images. By studying 300K+ user-shared images from two popular social networks, it is proven that the proposed computation framework reduces 90% of runtime with a comparable accurate with existing frameworks.
- Deepak Agarwal, Bee-Chung Chen, and Pradheep Elango. 2010. Fast online learning through offline initialization for time-sensitive recommendation. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 703--712. Google Scholar
Digital Library
- Vinti Agarwal and K. K. Bharadwaj. 2013. A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc. Network Anal. Min. 3, 3 (2013), 359--379.Google Scholar
Cross Ref
- Xavie Amatriain and Justin Basilico. 2013. System architectures for personalization and recommendation. Retrieved from The Netflix Techblog: http://techblog. netflix. com/2013/03/system-architectures-for. html (2013).Google Scholar
- Jeremy Buhler. 2001. Efficient large-scale sequence comparison by locality-sensitive hashing. Bioinformatics 17, 5 (2001), 419--428.Google Scholar
Cross Ref
- Ming Cheung and James She. 2014. Bag-of-features tagging approach for a better recommendation with social big data. In Proceedings of the 4th International Conference on Advances in Information Mining and Management (IMMM’14). 83--88.Google Scholar
- Ming Cheung, James She, and Zhanming Jie. 2015. Connection discovery using big data of user-shared images in social media. IEEE Trans. Multimedia (2015). Accepted.Google Scholar
- Ming Cheung, James She, and Li Xiaopeng. 2015. Non-user generated annotation on user-shared images for connection discovery. In Proceedings of The IEEE International Conference on Cyber, Physical and Social Computing (CPSCom’15).Google Scholar
Digital Library
- Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D Ullman, and Cheng Yang. 2001. Finding interesting associations without support pruning. IEEE Trans. Knowl. Data Eng. 13, 1 (2001), 64--78. Google Scholar
Digital Library
- Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine Learn. 20, 3 (1995), 273--297. Google Scholar
Digital Library
- Weijia Dai, Ginger Z. Jin, Jungmin Lee, and Michael Luca. 2012. Optimal Aggregation of Consumer Ratings: An Application to Yelp.com. Technical Report. National Bureau of Economic Research.Google Scholar
- Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web. ACM, 271--280. Google Scholar
Digital Library
- Claudia Diamantini, Domenico Potena, Alessandro Sabelli, and Samuele Scattolini. 2014. An integrated system for social information discovery. In Proceedings of the International Conference on Collaboration Technologies and Systems (CTS’14). IEEE, 353--360.Google Scholar
Cross Ref
- Eric Gilbert. 2012. Predicting tie strength in a new medium. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. ACM, 1047--1056. Google Scholar
Digital Library
- Eric Gilbert and Karrie Karahalios. 2009. Predicting tie strength with social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 211--220. Google Scholar
Digital Library
- Aristides Gionis, Piotr Indyk, Rajeev Motwani, and others. 1999. Similarity search in high dimensions via hashing. In Very Large Data Base, Vol. 99. 518--529. Google Scholar
Digital Library
- Liang Gou, Fang You, Jun Guo, Luqi Wu, and Xiaolong Luke Zhang. 2011. Sfviz: Interest-based friends exploration and recommendation in social networks. In Proceedings of the 2011 Visual Information Communication-International Symposium. ACM, 15. Google Scholar
Digital Library
- William H. Hsu, Andrew L. King, Martin S. R. Paradesi, Tejaswi Pydimarri, and Tim Weninger. 2006. Collaborative and structural recommendation of friends using weblog-based social network analysis. In Proceedings of the AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. 55--60.Google Scholar
- Piotr Indyk and Rajeev Motwani. 1998. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the 13th Annual ACM Symposium on Theory of Computing. ACM, 604--613. Google Scholar
Digital Library
- Junzhong Ji, Zhiqiang Sha, Chunnian Liu, and Ning Zhong. 2003. Online recommendation based on customer shopping model in e-commerce. In Proceedings of the IEEE International Conference on Web Intelligence (WIC’03). IEEE, 68--74. Google Scholar
Digital Library
- Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, and Tao Mei. 2015. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans. Multimedia 17, 6 (2015), 907--918.Google Scholar
Cross Ref
- Zhanming Jie, Ming Cheung, and James She. 2015. A cloud-assisted framework for bag-of-features tagging in social networks. In Proceedings of the IEEE 4th Symposium on Network Cloud Computing and Applications. Accepted. Google Scholar
Digital Library
- Jason J. Jones, Jaime E. Settle, Robert M. Bond, Christopher J. Fariss, Cameron Marlow, and James H. Fowler. 2013. Inferring tie strength from online directed behavior. PloS One 8, 1 (2013), e52168.Google Scholar
Cross Ref
- I. Kanellopoulos and G. G. Wilkinson. 1997. Strategies and best practice for neural network image classification. Int. J. Remote Sens. 18, 4 (1997), 711--725.Google Scholar
Cross Ref
- Xiaojiang Lei, Xueming Qian, and Guoshuai Zhao. 2016. Rating prediction based on social sentiment from textual reviews. IEEE Trans. Multimedia 18, 9 (2016), 1910--1921. Google Scholar
Digital Library
- Ian X. Y. Leung, Pan Hui, Pietro Lio, and Jon Crowcroft. 2009. Towards real-time community detection in large networks. Phys. Rev. E 79, 6 (2009), 066107.Google Scholar
Cross Ref
- Zhenyu Li, Jiali Lin, Kave Salamatian, and Gaogang Xie. 2013. Social connections in user-generated content video systems: Analysis and recommendation. IEEE Trans. Network Serv. Manage. 10, 1 (2013), 70--83.Google Scholar
Cross Ref
- Andy Liaw and Matthew Wiener. 2002. Classification and regression by randomForest. R News 2, 3 (2002), 18--22.Google Scholar
- David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 2 (2004), 91--110. Google Scholar
Digital Library
- Xueming Qian, He Feng, Guoshuai Zhao, and Tao Mei. 2014. Personalized recommendation combining user interest and social circle. IEEE Trans. Knowl. Data Eng. 26, 7 (2014), 1763--1777.Google Scholar
Cross Ref
- Xiuquan Qiao, Jianchong Su, Jinsong Zhang, Wangli Xu, Budan Wu, Sida Xue, and Junliang Chen. 2014. Recommending friends instantly in location-based mobile social networks. Commun. China 11, 2 (2014), 109--127.Google Scholar
Cross Ref
- Lara Quijano-Sanchez, Juan A. Recio-Garcia, and Belen Diaz-Agudo. 2011. Happymovie: A facebook application for recommending movies to groups. In Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence (ICTAI’11). IEEE, 239--244. Google Scholar
Digital Library
- Jitao Sang and Changsheng Xu. 2012. Right buddy makes the difference: An early exploration of social relation analysis in multimedia applications. In Proceedings of the 20th ACM International Conference on Multimedia. ACM, 19--28. Google Scholar
Digital Library
- Jitao Sang and Changsheng Xu. 2013. Social influence analysis and application on multimedia sharing websites. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 9, 1s (2013), 53. Google Scholar
Digital Library
- Xiance Si and Maosong Sun. 2009. Tag-LDA for scalable real-time tag recommendation. J. Comput. Info. Syst. 6, 1 (2009), 23--31.Google Scholar
Cross Ref
- Meredith M. Skeels and Jonathan Grudin. 2009. When social networks cross boundaries: A case study of workplace use of facebook and linkedin. In Proceedings of the ACM 2009 International Conference on Supporting Group Work. ACM, 95--104. Google Scholar
Digital Library
- Malcolm Slaney and Michael Casey. 2008. Locality-sensitive hashing for finding nearest neighbors {lecture notes}. IEEE Signal Process. Mag. 25, 2 (2008), 128--131.Google Scholar
Cross Ref
- Yang Song, Ziming Zhuang, Huajing Li, Qiankun Zhao, Jia Li, Wang-Chien Lee, and C. Lee Giles. 2008. Real-time automatic tag recommendation. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 515--522. Google Scholar
Digital Library
- D. T. Tsai, Allen Y. Chang, S. Chung, and You Sheng Li. 2010. A proxybased real-time protection mechanism for social networking sites. Proceedings of the International Carnahan Conference on Security Technology (ICCST’10).Google Scholar
- Stanley Wasserman. 1994. Social Network Analysis: Methods and Applications. Vol. 8. Cambridge University Press.Google Scholar
- Zhipeng Wu, Shuqiang Jiang, and Qingming Huang. 2009. Friend recommendation according to appearances on photos. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, 987--988. Google Scholar
Digital Library
- Rongjing Xiang, Jennifer Neville, and Monica Rogati. 2010. Modeling relationship strength in online social networks. In Proceedings of the 19th International Conference on World Wide Web. ACM, 981--990. Google Scholar
Digital Library
- Xing Xie. 2010. Potential friend recommendation in online social network. In Proceedings of the IEEE/ACM International Conference on Green Computing (GrenCom’10) and the International Conference on Cyber, Physical and Social Computing (CPSCom’10). IEEE, 831--835. Google Scholar
Digital Library
- Xiwang Yang, Yang Guo, and Yong Liu. 2013. Bayesian-inference-based recommendation in online social networks. IEEE Trans. Parallel Distrib. Syst. 24, 4 (2013), 642--651. Google Scholar
Digital Library
- Ting Yao, Chong-Wah Ngo, and Tao Mei. 2011. Context-based friend suggestion in online photo-sharing community. In Proceedings of the 19th ACM International Conference on Multimedia. ACM, 945--948. Google Scholar
Digital Library
- Guoshuai Zhao, Xueming Qian, and Xing Xie. 2016. User-service rating prediction by exploring social users’ rating behaviors. IEEE Trans. Multimedia 18, 3 (2016), 496--506. Google Scholar
Digital Library
- Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Xian-Sheng Hua, and Shipeng Li. 2011. Modeling social strength in social media community via kernel-based learning. In Proceedings of the 19th ACM International Conference on Multimedia. ACM, 113--122. Google Scholar
Digital Library
Index Terms
An Efficient Computation Framework for Connection Discovery using Shared Images
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