performs global summarization, which greatly improves the efficiency of the algorithm. This article mainly studies the distributed recommendation algorithm under the Hadoop platform. The recommendation algorithm combines the decision tree and the collaborative filtering algorithm, and improves the traditional collaborative filtering algorithm to improve the timeliness of recommendation.
INTRODUCTION TO RELATED TECHNOLOGIES
Introduction to the traditional collaborative filtering algorithm
International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 1–8
In order to improve the accuracy of the proposed algorithm in collaborative filtering recommendation system, an Improved Pearson collaborative filtering (IP-CF) algorithm is proposed in this paper. The algorithm uses the user portrait, item characteristics and data of user behavior to compute the baseline predictors model. Instead of the traditional algorithm’s similarity calculation, the prediction model is used to improve the accuracy of the recommendation algorithm. Experimental results on
International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 97–100
, database and ETL operation, which can calculate a set of complete recommendation system from user operation end to server and data calculation. In order to improve the accuracy of the recommendation algorithm, this paper introduces k-means clustering algorithm to improve the recommendation algorithm based on user-based collaborative filtering.The experimental results show that the accuracy of the proposed algorithm has been significantly improved after the introduction of k-means.
International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 126–132
This article introduces ensemble learning algorithms in recommender systems, and in boosting algorithm framework of this article, shows how to filter the basic recommendation algorithm according to the characteristics of boosting algorithm. By comparing the rational choice of the two recommended boosting algorithm is applied to the frame. And then it determines the main parameters of the algorithm through the experiments, ultimately to obtain a more effective integration of the recommendation
International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 368–386
filtering algorithms. This paper reviews the related research of collaborative filtering. Firstly, it expounds the connotation of collaborative filtering and its main situation, including sparsity, multi-content and scalability, and then detail the solutions for domestic and foreign scholars. This article is very helpful for the study and research of collaborative filtering algorithms. Qiang C, et al. has proposed a recommendation algorithm based on label and collaborative filtering. The label is used
International Journal of Advanced Network, Monitoring and Controls , ISSUE 4, 47–62