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Service Recommendation Based on Social Balance Theory and Collaborative Filtering

  • Lianyong Qi
  • Wanchun Dou
  • Xuyun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9936)

Abstract

With the increasing popularity of web service technology, many users turn to look for appropriate web services to further build their complex business applications. As an effective manner for service discovery, service recommendation technique is gaining ever-increasing attention, e.g., Collaborative Filtering (i.e., CF) recommendation. Generally, the traditional CF recommendation (e.g., user-based CF, item-based CF or hybrid CF) can achieve good recommendation results. However, due to the inherent sparsity of user-service rating data, it is possible that the target user has no similar friends and the services preferred by target user own no similar services. In this exceptional situation, traditional CF recommendation approaches cannot deliver an accurate recommendation result. In view of this shortcoming, a novel Social Balance Theory (i.e., SBT)-based service recommendation approach, i.e., Rec SBT is introduced in this paper, to help improve the recommendation performance. Finally, through a set of simulation experiments deployed on MovieLens-1M dataset, we further validate the feasibility of Rec SBT in terms of recommendation accuracy and recall.

Keywords

Service recommendation Target user Friend user Enemy user Social balance theory Collaborative filtering 

Notes

Acknowledgements

This paper is partially supported by Natural Science Foundation of China (No. 61402258), Key Research and Development Project of Jiangsu Province (No. BE2015154), China Postdoctoral Science Foundation (No. 2015M571739), Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2016B22), Open Project of State Key Laboratory of Software Engineering (No. SKLSE2014-10-03).

References

  1. 1.
    Wu, X., Cheng, B., Jun, L.C.: Collaborative filtering service recommendation based on a novel similarity computation method. IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2015.2479228
  2. 2.
    Xiang, Y.T., Jie, Z.: Dynamic personalized recommendation on sparse data. IEEE Trans. Knowl. Data Eng. 25(12), 2895–2899 (2013)CrossRefGoogle Scholar
  3. 3.
    Cartwright, D., Harary, F.: Structural balance: a generalization of Heider’s theory. Psychol. Rev. 63(5), 277 (1956)CrossRefGoogle Scholar
  4. 4.
    Qi, L., Zhang, X., Wen, Y., Zhou, Y.: A social balance theory-based service recommendation approach. In: Yao, L., et al. (eds.) APSCC 2015. LNCS, vol. 9464, pp. 48–60. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26979-5_4 CrossRefGoogle Scholar
  5. 5.
    Joseph, L.R., Alan, W.: Nicewander: thirteen ways to look at the correlation coefficient. Am. Stat. 42(1), 59–66 (1988)Google Scholar
  6. 6.
  7. 7.
    Zi, B.Z., Hao, M., Michael, R.L., Irwin, K.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)CrossRefGoogle Scholar
  8. 8.
    Lin, S.Y., Lai, C.H., Wu, C.H., Lo, C.C.: A trustworthy QoS-based collaborative filtering approach for web service discovery. J. Syst. Softw. 93, 217–228 (2014)CrossRefGoogle Scholar
  9. 9.
    Li, D., Chen, C., Lv, Q., et al.: An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Gener. Compt. Syst. 55, 311–320 (2016)CrossRefGoogle Scholar
  10. 10.
    Cao, J., Wu, Z., Wang, Y., et al.: Hybrid collaborative filtering algorithm for bidirectional web service recommendation. Knowl. Inf. Syst. 36(3), 607–627 (2013)CrossRefGoogle Scholar
  11. 11.
    Ming, D.T., Yu, X., Jian, X.L., Zi, B.Z., Frank, L.: Combining global and local trust for service recommendation. In: 21st IEEE International Conference on Web Services, pp. 305–312. IEEE Press, New York (2014)Google Scholar
  12. 12.
    Lian, Y.Q., Wan, C.D., Jin, J.C.: Weighted principal component analysis-based service selection method for multimedia services in cloud. Computing 98, 195–214 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  2. 2.State Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  4. 4.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand

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