Advertisement

A Trust-Based Prediction Approach for Recommendation System

  • Peng Wang
  • Haiping HuangEmail author
  • Jie Zhu
  • Lingtao Qi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10975)

Abstract

The recommendation system has been widely used in e-commerce, but still suffers from data sparsity and cold-start problems. This paper combines the user trust relationship with the collaborative filtering recommendation system and puts forward the recommendation approach based on trust delivery (TDR), in order to solve the above two problems. Through calculating the quantifying trust values between users, the prediction score of an unrated item can be figured out to achieve effective recommendation. Compared with other recommendation algorithms, TDR achieves better performance on standard Mean Absolute Error (MAE) and Coverage.

Keywords

Recommendation systems Trust relationship Collaborative filter 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of P. R. China (No. 61672297), the Key Research and Development Program of Jiangsu Province (Social Development Program, No. BE2017742).

References

  1. 1.
    Dakhel, A.M., Malazi, H.T., Mahdavi, M.: A social recommender system using item asymmetric correlation. Appl. Intell. 5, 1–14 (2017)Google Scholar
  2. 2.
    Zheng, Z., Ma, H., Lyu, M.R.: A collaborative filtering based web service recommender system. In: IEEE International Conference on Web Services, Los Angeles, CA, USA, 6–10 July 2009, pp. 437–444. IEEE, July 2009Google Scholar
  3. 3.
    Zhao, Y.S., Liu, Y.P., Zeng, Q.A.: A weight-based item recommendation approach for electronic commerce systems. Electron. Commer. Res. 17(2), 205–226 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhao, Z.D., Shang, M.S.: User-based collaborative-filtering recommendation algorithms on Hadoop. In: International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 9–10 January 2010, pp. 478–481. IEEE, January 2010Google Scholar
  5. 5.
    Hwang, C.-S., Chen, Y.-P.: Using trust in collaborative filtering recommendation. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS (LNAI), vol. 4570, pp. 1052–1060. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73325-6_105CrossRefGoogle Scholar
  6. 6.
    Shambour, Q., Lu, J.: A trust-semantic fusion-based recommendation approach for e-business applications. Decis. Support Syst. 54(1), 768–780 (2012)CrossRefGoogle Scholar
  7. 7.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: International Conference on Intelligent User Interfaces, San Diego, California, USA, 10–13 January 2005, pp. 167–174. ACM, January 2005Google Scholar
  8. 8.
    Cao, G., Kuang, L.: Identifying core users based on trust relationships and interest similarity in recommender system. In: IEEE International Conference on Web Services, San Francisco, USA, 27 June–2 July 2016, pp. 284–291. IEEE, June–July 2016Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nanjing University of Posts and TelecommunicationsNanjingChina

Personalised recommendations