# Research on Personalized Recommendation Methods for Online Video Learning Resources
## 1 Introduction  
E-Learning (electronic learning) breaks through the limitation of time and space, so that learners who want to learn knowledge can get learning materials, watch learning videos, and study as if they are in a school classroom. For some subjects, online learning is even better than traditional teaching. For example, in the field of medicine, students can watch some videos of the operation process repeatedly, which is better than the teacher's live demonstration. Thanks to its ease of use and unlimited access, e-learning improved students' clinical learning process by virtual visual support [1]. As a supplement to traditional teaching, video teaching also has its advantages [2]. In 2020, the sudden COVID-19 epidemic forced all kinds of distance learning platforms to develop quickly. It is, however, not easy to find the information they need quickly in the vast amount of e-learning resources. Personalized recommendations can effectively help learners to obtain learning resources quickly for their personal needs. Intelligent recommendation systems are more and more widely used in today's era of big data [3, 4]. There are intelligent recommendation algorithms applied in online shopping, online movie watching, and news pushing. At present, most e-learning systems have no recommendation function. This work will add personalized recommendation of video learning resources to the existing e-learning platform based on data mining and machine learning algorithm. Learners usually watch videos on the e-learning platform. Some learners know exactly what they want to watch. Some learners do not know which materials are more useful for them and just search at random. If the e-learning platform is equipped with an intelligent recommendation system then it can recommend more interesting learning videos according to users' preferences and optimize the learning experience for learners; also, improve learning efficiency and enhance the stickiness of the platform. We use machine learning algorithm to effectivelymine data from the video's ratings or evaluations given by the learners who have watched. Learners' interests are found and the corresponding learning videos are recommended to them. Some learners do not necessarily score or evaluate the video after learning. In this case, we use a priori algorithm of data mining to find the correlation among the learning materials, and recommend learning video materials to the corresponding learners. For the learners who have not watched videos, we recommend videos with a large number of views according to the subject category they choose when they register. This situation will not be described. In summary, our contributions in this study are as follows. * For the situation that the viewers do not give a score, we give the detailed process of searching the strong association relationship among the video learning materials by a priori algorithm in data mining. According to the strong association relationship of the video data, the videos are recommended to the related learners. * If the evaluation from the learners can be found in the e-learning platform, the collaborative filtering algorithm based on items is used to find out the highly similar video data by Pearson correlation coefficient method, and then the corresponding videos are recommended for the related learners. * These two methods complement each other. Most systems only use one recommendation method. We use two methods. All kinds of users can get personalized recommendation. If a system adopts these two methods at the same time, then its recommendation function is stronger than other systems. The rest of the paper is arranged as follows. The second section displays related work about research on the reform of education and teaching with technology of data mining and artificial intelligent. The third section introduces collaborative filtering algorithm and the a priori algorithm. In Section 4, we present how to implement personalized recommendation and their detailed processes.