Abstract
Recommendation methods have attracted extensive attention recently because they intent to alleviate the information overload problem. Among them, the social recommendation methods have become one of the popular research fields because they are benefit to solve the cold start problem. In social recommendation systems, some users are of great significance, because they usually have decisive impacts on the recommendation results. However, it is still lack of research on how the important users make influence to recommendation methods. This paper presents three types of important users and utilizes three social frequently-used recommendation methods to analyze the influence from multiple perspectives. The experiments demonstrate that all the recommendation methods achieve better performance with important users, and the important neighbor users have the greatest impact on the recommendation methods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Levitin, D.J.: The organized mind: thinking straight in the age of information overload. Dutton (2014)
Lee, A.R., Son, S.M., Kim, K.K.: Information and communication technology overload and social networking service fatigue: a stress perspective. Comput. Hum. Behav. 55, 51–61 (2016)
Yang, B., Lei, Y., Liu, J., Li, W.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1633–1647 (2017)
Tang, J., Xia, H., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013)
Goldberg, D., et al.: Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12), 61–70 (1992)
Breese, J.S., Heckerman, D., Kadie C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009(12), 4 (2009)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: ACM Conference on Recommender Systems, pp. 135–142 (2010)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: ACM Conference on Information and Knowledge Management, pp. 931–940 (2008)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203–210 (2009)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Forth International Conference on Web Search & Web Data Mining, pp. 287–296 (2011)
Shen, C.W.: Research and analysis on the influence of micro blogging users based on micro blogging data. Ph.D. thesis, Beijing University of Posts and Telecommunications (2013)
Song, C., Zhang, X.K., Jia, J., et al.: Ranking importance of user node in micro-blog social network. Comput. Eng. Des. 37(8), 2050–2056 (2016)
Chickering, D.M.: Learning equivalence classes of Bayesian network structures. In: Twelfth International Conference on Uncertainty in Artificial Intelligence, pp. 150–157 (1996)
Wang, H.X., Shan, Z., Liu, H.Y.: An importance analytical approach for online social network. J. Shanghai Jiaotong Univ. 47(7), 1055–1059 (2013)
Wen, J.H., Fang, Q.Q., Liu, L., et al.: Impact of social relationship on model-based social recommender system. J. Front. Comput. Sci. Technol. (2017)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(1), 207–244 (2009)
Cao, G., Kuang, L.: Identifying core users based on trust relationships and interest similarity in recommender system. In: IEEE International Conference on Web Services, pp. 284–291 (2016)
Lo, Y.Y., Liao, W., Chang, C.S.: Temporal matrix factorization for tracking concept drift in individual user preferences. Comput. Sci. (2015)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? Geosci. Model. Dev. 7(1), 1525–1534 (2014)
Acknowledgements
The work is supported by the Basic and Advanced Research Projects in Chongqing under Grant No. cstc2015jcyjA40049, and the Guangxi Science and Technology Major Project under Grant No. GKAA17129002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhao, Z., Gao, M., Yu, J., Song, Y., Wang, X., Zhang, M. (2018). Impact of the Important Users on Social Recommendation System. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-00916-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00915-1
Online ISBN: 978-3-030-00916-8
eBook Packages: Computer ScienceComputer Science (R0)