Abstract: In recent years,short video apps such as TikTok,Kwai,and WeiShi have achieved great success,and the number of videos taken by users and uploaded to the APP platform has skyrocketed.In this environment of information overload,mining and recommending videos of interest to users has become a problem faced by video publishing platforms.Therefore,it is particularly important to design efficient video recommendation algorithms for these platforms.Aiming at the problem of high sparseness and huge scale of datasets in the field of media big data mining and recommendation,a video recommendation algorithm for multidimensional feature analysis and filtering is proposed.First,feature extraction is performed on videos from multiple dimensions such as user behavior and video tags.Then,similarity analysis is performed to calculate the video similarity by weighting to obtain similar video candidate sets,the similar video candidate sets are filtered,and then several videos selected by ranking the highest rated videos are recommended to users.Finally,based on the MovieLens public data set,the video recommendation algorithm proposed in this paper is implemented by using python3 programming language.A large number of experiments on the data set show that compared with the traditional collaborative filtering algorithm,the video recommendation algorithm for multidimensional feature analysis and filtering proposed in the paper improves the accuracy of the recommendation results by 6%,the recall rate by 4%,and the coverage rate by 18%.The experimental data fully demonstrates that considering the similarity between videos from multiple dimensions,combined with large-scale matrix factorization technology,the problems of high sparseness and huge data volume of the data set are alleviated to some extent,thereby effectively improving the recommendation results accuracy,recall,and coverage.

Key words: Collaborative filtering, Feature extraction, Similarity analysis, Sparsity, Video recommendation