Combinatorial Testing Strategies for Privacy in Recommendation Engines

Abstract

Recommendation systems play an important role in helping users to discover relevant items out of large collections of content. However, the widespread use of information about people's personal preferences raises concerns about privacy. In this research paper, we examine the combinatorial testing strategies to enhance privacy protection in recommendation engines without the detriment of prediction performance. The approach proposed includes a systematic evaluation of combinations of recommendation algorithms and privacy mechanisms by using the MovieLens 20M dataset. The data set has 20,000,263 user ratings and 465,564 tag applications on 27,278 movies created by 138,493 users between January 1995 and March 2015, which currently serves as a popular benchmark for recommender system research. The proposed combinatorial privacy testing model is compared with collaborative filtering, matrix factorization and deep neural recommendation methods. Experimental results show that the performance of the proposed model reaches better results with accuracy of 94.18%, precision of 93.40%, recall of 92.96% and F1-score of 93.18%. The results demonstrate that combinatorial privacy test can be effectively used to increase the reliability of recommendations or protect the information of users.