Research on Context-Awareness Mobile SNS Recommendation Algorithm
ZHANG Zhi-Jun1,2,3, LIU Hong 1,2
1.School of Information Science and Engineering, Shandong Normal University, Jinan 250014 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250014 3.School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101
Abstract Although patterns of human activity show a large degree of freedom, they exhibit structural patterns subjected by geographic and social constraints. Aiming at various problems of personalized recommendation in mobile networks, a social network recommendation algorithm is proposed with a variety of context-aware information and combined with a series of social network analysis methods.Based on geographical location and temporal information, potential social relations among users are mined deeply to find the most similar set of users for the target user, then recommendations are carried out incorporating with social relations of the mobile users to effectively solve the problem of recommendation precision. The above study can not only help LBSN designers and developers to better understand their users and grasp their want, but also help to refine the design of their system to provide users with more appropriate applications and services.The experimental results on the real-world dataset verify the feasibility and effectiveness of the proposed algorithm, and it has higher prediction accuracy compared with existing recommendation algorithms.
[1] Zhang Z J, Liu H. Application and Research of Improved Probability Matrix Factorization Techniques in Collaborative Filtering. International Journal of Control and Automation, 2014, 7(8): 79-92 [2] Gu H S, Xie X, Lv Q, et al. Etree: Effective and Efficient Event Modeling for Real-Time Online Social Media Networks // Proc of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Lyon, France, 2011, I: 300-307 [3] Gu H S, Gartrell M, Zhang L, et al. AnchorMF: Towards Effective Event Context Identification // Proc of the 22nd ACM International Conference on Information and Knowledge Management. San Francisco, USA, 2013: 629-638 [4] Gu H S, Hang H J, Lv Q, et al. Fusing Text and Friendships for Location Inference in Online Social Networks // Proc of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Macau, China, 2012, I: 158-165 [5] Benevenuto F, Rodrigues T, Cha M, et al. Characterizing User Behavior in Online Social Networks // Proc of the 9th ACM SIGCOMM Conference on Internet Measurement. Chicago, USA, 2009: 49-62 [6] Meng X W, Hu X, Wang L C, et al. Mobile Recommender Systems and Their Applications. Journal of Software, 2013, 24(1): 91-108 (in Chinese) (孟祥武,胡 勋,王立才,等.移动推荐系统及其应用研究.软件学报, 2013, 24(1): 91-108) [7] Wang Y X, Qiao X Q, Li X F, et al. Research on Context-Awareness Mobile SNS Service Selection Mechanism. Chinese Journal of Computers, 2010, 33(11): 2126-2135(in Chinese) (王玉祥,乔秀全,李晓峰,等.上下文感知的移动社交网络服务选择机制研究. 计算机学报, 2010, 33(11): 2126-2135) [8] Guo L, Ma J, Chen Z M. Trust Strength Aware Social Recommendation Method. Journal of Computer Research and Development, 2013, 50(9): 1805-1813 (in Chinese) (郭 磊,马 军,陈竹敏.一种信任关系强度敏感的社会化推荐算法.计算机研究与发展, 2013, 50(9): 1805-1813) [9] Zheng V W, Cao B, Zheng Y, et al. Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach // Proc of the 24th AAAI Conference on Artificial Intelligence. Atlanta, USA, 2010: 236-241 [10] Gao H J, Tang J L, Liu H. Mobile Location Prediction in Spatio-Temporal Context // Proc of the Mobile Data Challenge by Nokia Workshop in conjunction with International Conference on Pervasive Computing . Newcastle, UK, 2012: 32-39 [11] Gong Y, Li Y, Jin D P, et al. A Location Prediction Scheme Based on Social Correlation // Proc of the 73rd IEEE Vehicular Technology Conference.Yokohama, Japan, 2011: 1-5 [12] Thanh N, Phuong T M. A Gaussian Mixture Model for Mobile Location Prediction // Proc of the IEEE International Conference on Research, Innovation and Vision for the Future. Hanoi, Vietnam, 2007: 152-157 [13] Adomavicius G, Tuzhilin A. Context-Aware Recommender Systems // Ricci F, Rokach L, Shapira B, et al, eds. Recommender Systems Handbook. New York, USA: Springer US, 2008: 217-253 [14] Adomavicius G, Sankaranarayanan R, Sen S, et al. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems, 2005, 23(1): 103-145 [15] Meng X W, Shi Y C, Wang L C, et al. Review on Learning Mobile User Preferences for Mobile Network Services. Journal on Communications, 2013, 34(2): 147-155 (in Chinese) (孟祥武,史艳翠,王立才,等.用户对移动网络服务偏好学习技术综述.通信学报, 2013, 34(2): 147-155) [16] Wang L C, Meng X W, Zhang Y J. Context-Aware Recommender Systems. Journal of Software, 2012, 23(1): 1-20 (in Chinese) (王立才,孟祥武,张玉洁.上下文感知推荐系统.软件学报, 2012, 23(1): 1-20) [17] Cheng Z Y, Caverlee J, Lee K, et al. Exploring Millions of Footprints in Location Sharing Services // Proc of the 5th International Conference on Weblogs and Social Media. Barcelona, Spain, 2011: 81-88 [18] Gao H J, Tang J L, Liu H. Exploring Social-Historical Ties on Location-Based Social Networks // Proc of the 6th International AAAI Conference on Weblogs and Social Media. Dublin, Ireland, 2012: 114-121
2015, Vol. 28 
