Policy recommendation system

The system uses enterprise users’ basic information, including location, sector, business scope, revenue, and past search records, to provide timely and accurate recommendations for relevant policies. This ensures that enterprises can access useful information that is tailored to their specific needs.

The system’s recommendation algorithm is inspired by the patent titled “Federal Bayesian Personalized Ranking Recommendation Method and System based on Multi-Krum.” This patent introduces a method that combines Bayesian inference with personalized ranking to enhance the accuracy and effectiveness of recommendations. By leveraging the Multi-Krum algorithm, the system further improves the reliability and robustness of the recommendations.

When enterprise users provide their basic information, such as their location, sector, and business scope, the system analyzes these details to understand their specific context. By considering factors such as regional regulations, industry-specific policies, and the scope of their operations, the system can identify the most relevant policies for each enterprise.

Additionally, the system takes into account the revenue information of enterprise users. This helps in recommending policies that align with the financial capabilities and resources of each enterprise. By tailoring the recommendations to the financial context of the enterprise, the system ensures that the policies suggested are realistic and feasible.

The system also utilizes past search records of enterprise users to enhance the accuracy of its recommendations. By analyzing the historical preferences and interests of each user, the system gains insights into their specific areas of focus. This allows the system to provide targeted recommendations that align with the enterprise’s past interests and search patterns. By leveraging this personalized approach, the system increases the likelihood that the recommended policies will be relevant and valuable to the enterprise.

Overall, the system’s goal is to provide enterprise users with useful and timely information about relevant policies. By considering their basic information, revenue, and past search records, the system can tailor its recommendations to the specific needs and context of each enterprise. The algorithm, inspired by the patent on Federal Bayesian Personalized Ranking Recommendation Method and System based on Multi-Krum, ensures that the recommendations are accurate, reliable, and robust. With this system in place, enterprises can make informed decisions and stay updated on policies that are relevant to their operations.