Research on Key Technologies and Applications of Recommendation System Based on Machine Learning

In the current highly information-based and digital social environment, numerous technical applications are constantly enriching the sources and communication channels of network information data, so that users are facing the problems of information explosion and information overload while enjoying the convenience of information acquisition. Therefore, the emergence and development of recommendation system has become the only way to solve this problem. However, the conventional recommendation system still has obvious lag when dealing with data sparsity and cold start, and it is difficult to achieve accurate recommendation. Based on the actual application requirements, this paper will comprehensively explore the feasibility of machine learning algorithm in the field of recommendation system, and propose a set of recommendation algorithm construction scheme based on machine learning to make up for the shortcomings of the existing recommendation system. Practice has proved that the hybrid recommendation algorithm model based on decision tree (DT), random forest (RF) and logistic regression (LR) can automatically combine and screen features, mine valuable feature information from sparse data, and use these feature information to make efficient classification prediction, and then generate more comprehensive and accurate recommendation results, so as to improve user satisfaction and the performance of recommendation system.