Amplified Locality-Sensitive Hashing for Privacy-Preserving Distributed Service Recommendation
With the ever-increasing volume of services registered in various web communities, service recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the service selection decisions of target users. However, traditional CF-based service recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed service recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate “False-positive” or “False-negative” recommended results; therefore, we amplify LSH by AND/OR operations to improve the recommendation accuracy. Finally, through a set of experiments deployed on a real distributed service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed recommendation approach named DistSR Amplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.
KeywordsDistributed service recommendation Collaborative Filtering Privacy-preservation Recommendation accuracy Amplified Locality-Sensitive Hashing
This paper is partially supported by the National Key Research and Development Program of China (No. 2017YFB1001800), Natural Science Foundation of China (Nos. 61402258, 61672276), UoA Faculty Research Development Fund (No. 3714668), Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2016B22).
- 4.Cao, G., Kuang, L.: Identifying core users based on trust relationships and interest similarity in recommender system. In: IEEE International Conference on Web Services, pp. 284–291 (2016)Google Scholar
- 5.Zhong, Y., Fan, Y., Tan, W., Zhang, J.: Web service recommendation with reconstructed profile from mashup descriptions. IEEE Trans. Autom. Sci. Eng. (2016)Google Scholar
- 6.Mashal, I., Chung, T.-Y., Osama, O.: Toward service recommendation in internet of things. In: IEEE International Conference on Ubiquitous and Future Networks, pp. 328–331 (2015)Google Scholar
- 8.Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. VLDB 99(6), 518–529 (1999)Google Scholar
- 10.Data Mining and Query Log Analysis for Scalable Temporal and Continuous Query Answering (2015). http://www.optique-project.eu/
- 12.Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: International Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
- 13.Zhu, J., He, P., Zheng, Z., Lyu, M.R.: A privacy-preserving QoS prediction framework for web service recommendation. In: IEEE International Conference on Web Services, pp. 241–248 (2015)Google Scholar
- 16.Yao, L., Sheng, Q.Z., Qin, Y., Wang, X., Shemshadi, A., He, Q.: Context-aware point-of-interest recommendation using tensor factorization with social regularization. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1007–1010 (2015)Google Scholar
- 17.Zhong, Y., Fan, Y., Huang, K., Tan, W., Zhang, J.: Time-aware service recommendation for mashup creation in an evolving service ecosystem. In: IEEE International Conference on Web Services, pp. 25–32 (2014)Google Scholar
- 18.Wu, C., Qiu, W., Zheng, A., Wang, X., Yang, X.: QoS prediction of web services based on two-phase k-means clustering. In: IEEE International Conference on Web Services, pp. 161–168 (2015)Google Scholar
- 19.Rong, H., Huo, S., Hu, C., Mo, J.: User similarity-based collaborative filtering recommendation algorithm. J. Commun. 35(2), 16–24 (2014)Google Scholar
- 24.Fletcher, K.K., Liu, X.F.: A collaborative filtering method for personalized preference-based service recommendation. In: IEEE International Conference on Web Services, pp. 400–407 (2015)Google Scholar