Research On Big Data Personalized Service Intelligent Recommendation Method Considering Privacy Protection

As the standards of living continue to rise,there is a growing demand for services.The services become more colorful when combined with the Internet.It is accompanied by the difficulty of people’s decision-making on services and the increasingly strong demand for individualization.Establishing intelligent recommendation of personalized services of big data is an important way to help users make service decisions and meet the needs of personalized services.However,the privacy of users is often leaked and used maliciously by untrusted recommendation systems or stolen by attackers,causing losses and risks to users.Therefore,the focus of the current research is how to adopt an effective big data personalized service intelligent recommendation method,while protecting the user’s private information.Firstly,this thesis studies the research status of big data personalized service intelligent recommendation,and proposes separately research from online and offline services on personalized intelligent recommendation methods that consider privacy protection,and specifically analyzes the big data personalized intelligent recommendation method and the existing privacy problems for online services and offline services.Secondly,the solutions are proposed.In terms of big data personalized intelligent recommendation algorithm,the traditional collaborative filtering method introduces the user’s personal feature model to construct online service recommendation algorithm,and further introduces location information to build a big data personalized intelligent recommendation algorithm for offline services in order to improve the accuracy of recommendation.In terms of privacy protection,there exist the privacy requirements of user information big data on online services and offline services and their current privacy protection issues: how to ensure the balance between privacy protection and intelligent recommendation accuracy better,and how to achieve privacy protection of location information type and achieve integration with personalized offline services intelligent recommendation algorithms and other issues.This thesis applies the improved random disturbance method and location protection method to the big data personalized service intelligent recommendation.Furthermore,this thesis constructs the personalized intelligent recommendation methods of big data that consider privacy protection in the corresponding scenario.Finally,the practicability and effectiveness of the method proposed in this thesis are verified through design experiment analysis.