TruRec: An Improved Trust-Based Recommendation in Cross-Domain

  • Wanrong Gu
  • Xianfen XieEmail author
  • Ziye Zhang
  • Yichen He
  • Yijun Mao
  • Hailiang Li
  • Shishi Huang
  • Zaoqing Liang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


In Social Network, the research of recommendation system and trust relation can improve the accuracy of recommendation. The research of traditional recommendation algorithm based on trust relation is usually based on a single domain of interest without cross-domain research. In the real world, there are often multiple areas of interest between users. Based on this reality, this paper proposes a multi-interest domain recommendation framework based on trust relationship, and obtains better recommendation effect by solving the trust relationship. The experimental results show that the proposed method is superior to the traditional methods.


Recommendation Trust-based Multi-interest 



This work was financially supported by Guangdong Natural Science Foundation Project (2018A030313437) Ministry of Education Humanities and Social Sciences Research Youth Fund Project (18YJCZH037) and Guangdong Science and Technology Program Project (2018A070712021).


  1. 1.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering (2007)Google Scholar
  2. 2.
    Chirita, P.-A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, pp. 67–74. ACM (2005)Google Scholar
  3. 3.
    Golbeck, J.A.: Computing and applying trust in web-based social networks (2005)Google Scholar
  4. 4.
    Guy, I.: Social recommender systems. In: International Conference Companion on World Wide Web (2011)Google Scholar
  5. 5.
    Hao, M., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble (2009)Google Scholar
  6. 6.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM (2009)Google Scholar
  7. 7.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: ACM Conference on Recommender Systems (2010)Google Scholar
  8. 8.
    Kautz, H., Selman, B., Mehul, S.: Combining social networks and collaborative filtering. Commun. ACM 40(3), 63–65 (1997)CrossRefGoogle Scholar
  9. 9.
    King, I., Lyu, M.R., Hao, M.: Introduction to social recommendation. In: International Conference on World Wide Web (2010)Google Scholar
  10. 10.
    Massa, P., Avesani, P.: Trust metrics on controversial users: balancing between tyranny of the majority. Int. J. Semant. Web Inf. Syst. 3(1), 39–64 (2007)CrossRefGoogle Scholar
  11. 11.
    Qiu, T., Chen, G., Zhang, Z.K., Zhou, T.: An item-oriented recommendation algorithm on cold-start problem. In: International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (2011)Google Scholar
  12. 12.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009(12), 1–19 (2009)CrossRefGoogle Scholar
  13. 13.
    Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world (2012)Google Scholar
  14. 14.
    Yang, X., Steck, H., Yong, L.: Circle-based recommendation in online social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wanrong Gu
    • 1
  • Xianfen Xie
    • 2
    Email author
  • Ziye Zhang
    • 3
  • Yichen He
    • 1
  • Yijun Mao
    • 1
  • Hailiang Li
    • 2
  • Shishi Huang
    • 1
  • Zaoqing Liang
    • 1
  1. 1.South China Agriculture UniversityGuangzhouChina
  2. 2.Jinan UniversityGuangzhouChina
  3. 3.South China University of TechnologyGuangzhouChina

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