Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering (2007)
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)
Golbeck, J.A.: Computing and applying trust in web-based social networks (2005)
Guy, I.: Social recommender systems. In: International Conference Companion on World Wide Web (2011)
Hao, M., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble (2009)
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)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: ACM Conference on Recommender Systems (2010)
Kautz, H., Selman, B., Mehul, S.: Combining social networks and collaborative filtering. Commun. ACM 40(3), 63–65 (1997)
King, I., Lyu, M.R., Hao, M.: Introduction to social recommendation. In: International Conference on World Wide Web (2010)
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)
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)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009(12), 1–19 (2009)
Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world (2012)
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)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, W. et al. (2020). TruRec: An Improved Trust-Based Recommendation in Cross-Domain. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)