Intelligent recommendation system based on decision model of archive translation tasks - Lilan, Chen; Yongsheng, Chen (Frontiers in Computational Neuroscience, 2022)
How to recruit, test and train the intelligent recommender system users, and how to assign the archive translation tasks to all intelligent recommender system users according to the intelligent matching principle are still a problem that needs to be solved. With the help of proper names and terms in China’s Imperial Maritime Customs archives, this paper aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommender system will also improve the efficiency of archive translation task intelligent recommender quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommender system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this paper, through multi-label classification, the intelligent recommender system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommender system can improve the performance of task allocation over time without the participation of task issuers.