Abstract
With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRec two-LSH , in this paper. Concretely, in SerRec two-LSH , we first look for the “similar friends” of a target user through a privacy-preserving two-phase LSH process; afterwards, we determine the services preferred by the “similar friends” of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.
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Notes
- 1.
Actually, CF includes user-based CF, item-based CF and Hybrid CF; however, for simplicity, only user-based CF is discussed in this paper as the rationales of these three CF variants are similar.
References
Naim, H., Aznag, M., Quafafou, M., Durand, N.: Probabilistic approach for diversifying web services discovery and composition. In: 23rd International Conference on Web Services, pp. 73–80. IEEE, San Francisco (2016)
Zhang, N., Wang, J., Ma, Y.: Mining domain knowledge on service goals from textual service descriptions. IEEE Trans. Serv. Comput. doi:10.1109/TSC.2017.2693147
Wang, J., Zhu, Z., Liu, J., Wang, C., Xu, Y.: An approach of role updating in context-aware role mining. Int. J. Web Serv. Res. 14(2), 24–44 (2017)
Rong, H., Huo, S., Hu, C., Mo, J.: User similarity-based collaborative filtering recommendation algorithm. J. Commun. 35(2), 16–24 (2014)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. VLDB 99(6), 518–529 (1999)
Chung, K., Lee, D., Kim, K.J.: Categorization for grouping associative items using data mining in item-based collaborative filtering. Multimedia Tools Appl. 71(2), 889–904 (2014)
Jiang, C., Duan, R., Jain, H.K., Liu, S., Liang, K.: Hybrid collaborative filtering for high-involvement products: a solution to opinion sparsity and dynamics. Decis. Support Syst. 79, 195–208 (2015)
Wang, X., Zhu, J., Zheng, Z., Song, W., Shen, Y., Lyu, M.R.: A spatial-temporal QOS prediction approach for time-aware web service recommendation. ACM Trans. Web 10(1), 1–25 (2016)
Yu, C., Huang, L.: A web service QOS prediction approach based on time- and location-aware collaborative filtering. Serv. Oriented Comput. Appl. 10(2), 135–149 (2016)
Fletcher, K.K., Liu, X.F.: A collaborative filtering method for personalized preference-based service recommendation. In: 22nd International Conference on Web Services, pp. 400–407. IEEE, New York (2015)
Wang, J.H., Chen, Y.H.: A distributed hybrid recommendation framework to address the new-user cold-start problem. In: UIC-ATC-ScalCom, pp. 1686–1691. IEEE, Beijing (2015)
Tang, M., Dai, X., Cao, B., Liu, J.: WSWalker: a random walk method for QOS-aware web service recommendation. In: 22nd International Conference on Web Services, pp. 591–598. IEEE, New York (2015)
Dou, W., Zhang, X., Liu, J., Chen, J.: HireSome-II: towards privacy-aware cross-cloud service composition for big data applications. IEEE Trans. Parallel Distrib. Syst. 26(2), 455–466 (2015)
Zhu, J., He, P., Zheng, Z., Lyu, M.R.: A privacy-preserving QOS prediction framework for web service recommendation. In: 22nd International Conference on Web Services, pp. 241–248. IEEE, New York (2015)
Li, D., Chen, C., Lv, Q., Shang, L., Zhao, Y., Lu, T., Gu, N.: An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Gener. Comput. Syst. 55, 311–320 (2016)
Joseph, L.R., Alan, N.W.: Thirteen ways to look at the correlation coefficient. Am. Stat. 42(1), 59–66 (1988)
Broder, A.Z.: On the resemblance and containment of documents. In: Compression and Complexity of Sequences, pp. 21–29. IEEE, Salerno (1997)
Ioannidis, Y., et al.: Data mining and query log analysis for scalable temporal and continuous query answering (2015). http://www.optique-project.eu/
Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QOS of real world web services. IEEE Trans. Serv. Comput. 7(1), 32–39 (2014)
Breese J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. IEEE, Madison (1998)
Acknowledgement
This paper is partially supported by Natural Science Foundation of China (No. 61402258, 61672276), key Research and Development Project of Jiangsu Province (No. BE2015154, No. BE2016120), Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2016B22).
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Qi, L., Dou, W., Zhang, X. (2017). Two-Phase Locality-Sensitive Hashing for Privacy-Preserving Distributed Service Recommendation. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_13
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