Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

Abstract

With the rapid development of internet plus education, it is increasingly important to quickly and accurately match personalized videos for learners from massive learning videos. However, the existing resource recommendation methods have two problems. On the one hand, they do not make full use of the implicit interaction data of learners in the learning process, i.e., they rarely reflect the comprehensive consideration of learners' interest preferences and cognitive level when describing learners. On the other hand, they are mostly based on collaborative filtering algorithms, using the similarity between learners or videos to recommend, ignoring the impact of semantic relations between videos on the recommendation results. In view of this, we proposed a personalized learning video matching method (IFT‐PTransE) based on heterogeneous feature data transfer and knowledge reasoning from two aspects of learning behavior data mining and knowledge graph representation learning. In this method, firstly, a learner model is constructed. By analyzing and quantifying various implicit interaction behavior data of learners, the sparse video scoring matrix is filled as auxiliary data to complete the transfer of target scoring data. Secondly, the semantic close relationship between videos is introduced. PTransE algorithm is used to mine the multi‐path relationship between entities. All entity relationships are embedded into the low dimensional vector space, so that the semantic similarity between videos can be calculated using the distance between vectors. Finally, video score similarity and semantic similarity between frequencies are fused. video sorting is performed based on the improved collaborative filtering algorithm and then recommend the top N videos to the students. Through simulation and analysis, the effectiveness of this method in personalized video matching is proved. This method makes up for the shortcomings of collaborative filtering algorithm in using implicit information, enhances the recommendation effect at the semantic level, and solves the data sparse and cold start problems to a certain extent. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.