Research on big data mining based on improved parallel collaborative filtering algorithm

In the big data age, the traditional parallel collaborative filtering algorithm cannot meet the needs of data analysis in the efficiency and accuracy of data processing. Therefore, this paper improves the traditional parallel collaborative filtering algorithm, analyzes the execution flow of the collaborative filtering algorithm, discusses the shortcomings of the traditional parallel collaborative filtering algorithm, and then describes in detail the steps of improved the collaborative filtering algorithm from generating nodes scoring vectors, obtaining neighboring nodes and forming recommendation information. Finally, the improved parallel collaborative filtering algorithm is verified through three aspects of running time, speedup and recommended accuracy. Experimental results show that the improved parallel collaborative filtering algorithm proposed in this paper has better running efficiency and higher recommendation accuracy than traditional parallel algorithm based on co-occurrence matrix.