Research And Design Personalized Recommendation System Based On Data Mining

In modern times, more and more new products and technologies emerged in people’s vision, especially online shopping. As so many people shopping at network, huge amounts of data of electronic commerce fields appeared, which included displaying data and implicit data. People often confused of these data when they faced them, and businesses are eager to find the potential law to create greater economic benefits. Personalized recommendation system has been applied by many large e-commerce sites to help users find commodity they are interested, which can enhance the rate of purchase and the viscosity of users. One of the most important expression of personalized recommendation is intelligent recommendation system, which is irreplaceable in electronic commerce fields.Many researchers recommend a variety of recommended technologies which have their own merits. But as the development and application of these single recommendation system, the sparsity and cold start of data are more prominent, which seriously affected the recommendation quality. Through the development of technologies, people find that the hybrid recommendation engine has better recommendation quality than single recommendation engine.In this study, We use principal component analysis to reduce the sparse user-item matrix and use Fuzzy Art neural network to cluster to improve the recommendation quality. We also use KdTree to storage the clustering results to improve the search speed and the similarity of the neighbor users. Compared to traditional recommendation algorithms, the recommended model in this study improves the recommended accuracy and effective eases the sparsity problem of data.