Distributed collaborative filtering recommendation algorithm based on DHT
- 109 Downloads
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.
KeywordsDistributed collaborative filtering recommendation algorithm Similar neighbors Fuzzy critical values
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.
- 1.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)Google Scholar
- 2.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)Google Scholar
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.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
- 6.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)Google Scholar
- 7.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
- 8.Kaleli, C., Polat, H.: Privacy-preserving naïve Bayesian classifier-based recommendations on distributed data. Comput. Intell. 31(1), 47–68 (2015)Google Scholar
- 9.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
- 10.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)Google Scholar