Title: Recommending Good Web Docs based on User's preferences and既往病史 (English Translation)
Introduction:
WebDocs, or online medical journals, has become an essential tool for healthcare professionals to stay up-to-date with the latest medical advancements. However, the quality of WebDocs can vary greatly, and it can be challenging for healthcare professionals to find the most relevant and accurate articles. In this paper, we propose a user-centered approach to recommend good WebDocs based on user's preferences and既往病史.
Research Questions:
1. What are the factors that influence the quality of WebDocs?
2. How can we use user's preferences and既往病史 to recommend good WebDocs?
Methodology:
We conducted a literature review to identify the factors that influence the quality of WebDocs. We then developed a user-centered approach to recommend good WebDocs based on user's preferences and既往病史. Our approach involves using a combination of machine learning and user-centered design techniques.
Results:
Our results show that user's preferences (如疾病类型、期刊级别、作者国籍等) and既往病史 (如医生的职业史、过敏史、药物过敏史等) are important factors that influence the quality of WebDocs. By using these preferences and既往病史， we can recommend good WebDocs that are more relevant and accurate.
Conclusion:
In conclusion, our study suggests that using user's preferences and既往病史 to recommend good WebDocs is a user-centered approach that can improve the quality of online medical journals. Future research can explore the performance of this approach in real-world scenarios.