Skip to main content
SpringerLink
Log in
Book cover

Asia-Pacific Services Computing Conference

APSCC 2016: Advances in Services Computing pp 357–374Cite as

Combining Social Balance Theory and Collaborative Filtering for Service Recommendation in Sparse Environment

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10065))

Abstract

With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services, through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, the CF-based recommendation approaches can work well, when the target user has similar friends or the target services (i.e., the services preferred by target user) have similar services. However, in certain situations when user-service rating data is sparse, it is possible that target user has no similar friends and target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result, which brings a great challenge for accurate service recommendation. In view of this challenge, we combine Social Balance Theory (i.e., SBT) and CF to put forward a novel recommendation approach Rec SBT+CF . Finally, the feasibility of our proposal is validated, through a set of simulation experiments deployed on MovieLens-1M dataset.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wang, J., Gao, P., Ma, Y., He, K.: Common topic group mining for web service discovery. In: Yao, L., Xie, X., Zhang, Q., Yang, L.T., Zomaya, A.Y., Jin, H. (eds.) APSCC 2015. LNCS, vol. 9464, pp. 92–107. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26979-5_7

    Chapter  Google Scholar 

  2. Fei, H., Mo, T., Wang, Y., Wu, Z., Liu, Y., Kuang, L.: The searching ranking model based on the sharing and recommending mechanism of social network. In: Yao, L., Xie, X., Zhang, Q., Yang, L.T., Zomaya, A.Y., Jin, H. (eds.) APSCC 2015. LNCS, vol. 9464, pp. 222–234. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26979-5_16

    Chapter  Google Scholar 

  3. Zapater, J.J.S., Escrivá, D.M.L., García, F.R.S., Durá, J.J.M.: Semantic web service discovery system for road traffic information services. Expert Syst. Appl. 42(8), 3833–3842 (2015)

    Article  Google Scholar 

  4. Qi, L., Dou, W., Chen, J.: Weighted principal component analysis-based service selection method for multimedia services in cloud. Computing 98(1–2), 195–214 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers, San Francisco (1998)

    Google Scholar 

  6. Cartwright, D., Harary, F.: Structural balance: a generalization of Heider’s theory. Psychol. Rev. 63(5), 277 (1956)

    Article  Google Scholar 

  7. Sarwar, B., Karypis, G., Konstan J., et al.: Item-based collaborative filtering recommendation algorithms. In: 10th International Conference on World Wide Web, pp. 285–295. ACM Press, New York (2001)

    Google Scholar 

  8. Qi, L., Zhang, X., Wen, Y., Zhou, Y.: A social balance theory-based service recommendation approach. In: Yao, L., Xie, X., Zhang, Q., Yang, L.T., Zomaya, A.Y., Jin, H. (eds.) APSCC 2015. LNCS, vol. 9464, pp. 48–60. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26979-5_4

    Chapter  Google Scholar 

  9. Ma, Y., Wang, S., Hung, P.C., Hsu, C.H., Sun, Q., Yang, F.: A highly accurate prediction algorithm for unknown web service QoS values. IEEE T. Serv. Comput. 9(4), 911–923 (2016)

    Google Scholar 

  10. Gong, M., Xu, Z., Xu, L., Li, Y., Chen, L.: Recommending web service based on user relationships and preferences. In: 20th IEEE International Conference on Web Services, pp. 380–386. IEEE Press, New York (2013)

    Google Scholar 

  11. MovieLens-1M Dataset. http://www.grouplens.org/datasets/movielens/

  12. Mayer, D., Butler, D.: Statistical validation. Ecol. Model. 68(1), 21–32 (1993)

    Article  Google Scholar 

  13. Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Tang, M., Xu, Y., Liu, J., Zheng, Z., Liu, F.: 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 

  16. Jiang, D., Guo, X., Gao, Y., Liu, J., Li, H., Cheng, N.: Locations recommendation based on check-in data from location-based social network. In: 22nd International Conference on Geoinformatics, pp. 1–4. IEEE Press, New York (2014)

    Google Scholar 

  17. Cao, B., Liu, J., Tang, M., Zheng, Z., Wang, G.: mashup service recommendation based on user interest and social network. In: 20th IEEE International Conference on Web Services, pp. 99–106. IEEE Press, New York (2013)

    Google Scholar 

Download references

Acknowledgments

This paper is partially supported by Natural Science Foundation of China (No. 61402258), China Postdoctoral Science Foundation (No. 2015M571739), Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2016B22).

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, The Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Lianyong Qi, Wanchun Dou & Xuyun Zhang

  2. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China

    Lianyong Qi

  3. Department of Electrical and Computer Engineering, University of Auckland, Auckland, 1023, New Zealand

    Xuyun Zhang

Authors
  1. Lianyong Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanchun Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuyun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianyong Qi .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. North China University of Technology, Beijing, China

    Yanbo Han

  3. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Qi, L., Dou, W., Zhang, X. (2016). Combining Social Balance Theory and Collaborative Filtering for Service Recommendation in Sparse Environment. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49178-3_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49177-6

  • Online ISBN: 978-3-319-49178-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation