With the gradual popularization of social informatization, people's information security is gradually threatened. The development of the Internet has gradually exposed people's privacy, and the protection of t...
详细信息
With the gradual popularization of social informatization, people's information security is gradually threatened. The development of the Internet has gradually exposed people's privacy, and the protection of the privacy of people in the era of Internet information has become a topic of concern to all people. This research mainly discusses the research of the top-n recommendation algorithm with integrated neural network. The purpose of protecting people's privacy is achieved by interfering with the top-n recommendation algorithm on the Internet signal. In response to people's concerns, the top-n recommendation algorithm with integrated neural network was used during the experiment. The experimenters were randomly selected from netizens who frequently used computers to measure the privacy and security of each group of researchers and the signal of the top-n recommendation algorithm. Interference level. The use of the top-n recommendation algorithm is divided into six levels, and the experimentally measured information protection rate is 88% when the use level of the top-n recommendation algorithm is F level. In the case of signal interference, the interference intensity is divided into five levels. Similarly, when the signal interference intensity is 5, the information leakage rate is at least 10%. The selection of personnel throughout the experiment is random and the interference during the experiment and the use of the top-n recommendation algorithm with integrated neural network are divided according to levels. The research results show that when the signal interference intensity is 5 and the recommended algorithm is F, the privacy protection of netizens is the best. The top-n recommendation algorithm with integrated neural network has important potential value in protecting people's privacy.
China is vigorously developing electric vehicles, and the penetration rate of new energy vehicles in China has exceeded 10%. The penetration rates in the United States, Europe, and other regions are also growing, lead...
详细信息
ISBN:
(纸本)9798400707032
China is vigorously developing electric vehicles, and the penetration rate of new energy vehicles in China has exceeded 10%. The penetration rates in the United States, Europe, and other regions are also growing, leading to a high-speed increase in the demand for charging piles. Although the number of public charging piles has been increasing year by year, big data shows that the utilization rate of public charging piles is less than 15%. Therefore, how to guide users to avoid peak electricity consumption periods without compromising the convenience of charging for electric vehicle users is key to solving the problem. Finding an appropriate scheduling strategy is the core of solving this dilemma. For personalized recommendationalgorithms for charging piles, we propose a top-n recommendation algorithm for charging piles based on a neural collaborative filtering framework that combines multiple feature fusion methods. This algorithm uses concatenation and outer product operations to construct interaction relationships between users and items, respectively. Then, two learners are used to learn their interactive features, and finally, the learned prediction vectors are merged through concatenation. This approach not only fully excavates the potential information of embedded vectors but also has nonlinear feature fitting capabilities, thereby better improving the model's recommendation performance.
The emergence of mashup is gaining tremendous popularity and its application can be seen in a large number of domains. Along with the development of mashup technology, several mashup editors have been produced by the ...
详细信息
暂无评论