Personalized Recommendation of English Chinese Translation Teaching Information Resources Based on Transfer Learning

Abstract

With the rapid development of information technology, the field of English Chinese translation teaching has accumulated a large amount of information resources. The quantity of these teaching information resources is huge and diverse, and students and teachers face the problem of information overload, making it difficult to find resources that are suitable for their needs. Personalized recommendation technology has emerged to solve the problem of information overload, recommending resources that match users’ personal interests and needs from a vast amount of resources. In response to the problem of poor personalized recommendation effectiveness in the existing personalized recommendation methods for English Chinese translation teaching information resources, this article posits a fresh individualized suggestion method for informative resources in regards to teaching the translation of English to Chinese. This paper constructs a state perception model of information resources for E-C edge teaching based on transfer learning. Based on this, obtain student group information, English Chinese translation teaching information resource information, and resource rating information, cluster English Chinese translation teaching information resources, and construct a personalized recommendation model for English Chinese translation teaching information resources. The experimental results show that the information resource clustering effect of this method is good, the diversity of resource recommendations is better, and the F-Measure value is higher.