Multi-dimensional Similarity Personalized Recommendation Model in Deep Learning Mode

Abstract

Improving the recommendation accuracy is the ultimate goal of the recommendation system. Based on the recommendation accuracy, a variety of similarity calculation methods are compared and analyzed. Combined with the deep learning mode, a multi-dimensional similarity personalized recommendation model is built in the deep learning mode. Coefficient parameters, repeated training of training and adjustment coefficients in the deep learning training center, determine the personalized recommendation model scheme, through the simulation experiment, the proposed personalized recommendation model can effectively determine the user recommendation list and improve the accuracy of personalized recommendation.