Research On Context-aware Recommendation Algorithm Based On Differential Privacy Technology

In recent years,the personalized recommendation system has been the focus of attention because it can alleviate the problem of information overload.At present,the application of the recommendation system can be seen in almost all areas of the Internet.It can enable users to quickly find the information they need in massive amounts of data,saving users a lot of information search time.Studies have shown that incorporating relevant contextual information into traditional recommendation systems is beneficial to improve the accuracy of recommendation results and user satisfaction.If context recommendation wants to obtain higher recommendation accuracy,it is necessary to collect and use a large amount of user-related context information,but this will leak more users' personal privacy,which is also a problem that users worry about.If privacy and security issues are not taken into consideration before data collection and use,then an attacker is likely to obtain user's sensitive information directly or indirectly.If this information is used illegally by the attackers,it will cause a lot of economic and mental losses,and also pose a serious threat to people's daily lives.Therefore,the issue of personal privacy protection is the focus of attention in the recommendation system,and is also one of the current research hotspots.Among the many privacy protection technologies,the differential privacy protection technology has received significant attention because it can resist attacks by attackers with background knowledge.It is a privacy protection technology that has been proved by strict reasoning and can provide a strong guarantee for the user's privacy.Although differential privacy technology has been approved and used by researchers,there are still deficiencies in the research in context recommendation system.Therefore,in order to further protect the privacy of users in the recommendation system,this article combines differential privacy and context recommendation algorithms,and designs related differential privacy protection methods for different context information processing methods.This paper mainly conducts the following research:(1)According to the privacy protection problem when modeling user's contextual interest,this paper adopts a context recommendation algorithm based on differential privacy and Bayesian network.Because different context information has different effects on each user,Bayesian technology is used to integrate the context information into the recommendation system with different probabilities,find the user's interest in multi-dimensional context,and improve the user Similarity calculation method.Through differential privacy protection in the process of calculatingthe average user score and user similarity,and based on the problem of data sparsity,the clustering algorithm is used to cluster the items.Finally,the experiment proves that the context recommendation algorithm proposed in this paper can slightly improve the accuracy of recommendation on the basis of ensuring user privacy.(2)When making personalized recommendations to users,considering the similarity between historical contexts will improve the recommendation accuracy to a certain extent,but in the process of calculating context similarity,it will cause relevant information leakage.Based on this problem,this paper uses a recommendation algorithm based on differential privacy and context similarity.If multiple context information is similar,then the user's rating of this item under one context condition is also applicable to another context environment similar to it.The association between the context and the project is used to calculate the similarity between the contexts,and the user scoring matrix and the use of differential privacy technology in the context similarity calculation process to add corresponding noise for privacy protection.Finally,the experiment proves the effectiveness of the algorithm.