Abstract
Aiming at the problem that traditional similarity calculation method is not enough to capture the similarity relationship between users, a distributed collaborative filtering recommendation algorithm based on DHT is proposed for active users in distributed recommendation system to locate nearest neighbors. The algorithm searches the user’s similar neighbor information according to the “fuzzy critical value” generated by the user’s extreme score, so as to improve the search efficiency of the user similar neighbors. According to the distribution of user information, the calculation method of user neighbor similarity is improved. The similarity calculation is weighted, and the setting of the weights takes into account the two factors: the similarity degree between users and the inverse user information frequency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Deng, S.G., Huang, L.T., Wu, J., Wu, Z.H.: Trust-based personalized service recommendation: a network perspective. J. Comput. Sci. Technol. 29(1), 69 (2014)
Zheng, Z., Liu, J., Wang, P., Sun, S.: Collaborative filtering, time-weighted, uncertain neighbors, trustworthy subset. Recommendation system. Comput. Sci. 41(8), 7–12 (2014)
Wang, M., Ma, J.: A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft Comput. 20(10), 3981–3990 (2016)
Zheng, Z., Huang, T., Zhang, H., et al.: Towards a resource migration method in cloud computing based on node failure rule. J. Intell. Fuzzy Syst. 31(5), 2611–2618 (2016)
Zheng, Z., Zheng, Z.: Towards an improved heuristic genetic algorithm for static content delivery in cloud storage. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.06.011. Accessed 28 June 2017
Baraglia, R., Dazzi, P., Mordacchini, M., Ricci, L.: A peer-to-peer recommender system for self-emerging user communities based on gossip overlays. J. Comput. Syst. Sci. 79(2), 291–308 (2013)
Shi, X., Zheng, Z., Zhou, Y., Jin, H., He, L., Liu, B., Hua, Q.-S.: Graph processing on GPUs: a survey. ACM Comput. Surv. (2017). https://doi.org/10.1145/XXXXX
Kaleli, C., Polat, H.: Privacy-preserving naïve Bayesian classifier-based recommendations on distributed data. Comput. Intell. 31(1), 47–68 (2015)
Zheng, Z., Jeong, H.Y., Huang, T., et al.: KDE based outlier detection on distributed data streams in multimedia network. Multimed. Tools Appl. 76(17), 18027–18045 (2017). https://doi.org/10.1007/s11042-016-3681-y
Dooms, S., Audenaert, P., Fostier, J., De Pessemier, T., Martens, L.: In-memory, distributed content-based recommender system. J. Intell. Inf. Syst. 42(3), 645–669 (2014)
Acknowledgements
We thank the anonymous reviewers and the editors for the valuable feedback on earlier versions of this paper. This paper is supported by the National Statistical Science Research Project of China, under Grant Number 2015LY43.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, T., Wang, M. Distributed collaborative filtering recommendation algorithm based on DHT. Cluster Comput 22 (Suppl 2), 2931–2941 (2019). https://doi.org/10.1007/s10586-018-1699-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-1699-9