Intelligent recommendation system based on decision model of archive translation tasks

Abstract

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How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles 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 manuscript 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 recommendation system will also improve the efficiency of intelligent recommendation 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 recommendation 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 manuscript, through multi-label classification, the intelligent recommendation 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 recommendation system can improve the performance of task allocation over time without the participation of task issuers.

Keywords

• intelligent recommender system
• multi-label classification
• decision model
• classification algorithm
• adaptive