Research on Personalized Recommendation Model of E-commerce Based on Multimodal Big Data Analysis

E-commerce utilizes users' implicit behavior data to replace sparse ratings and strengthens the diversity optimization objective in the recommendation model to simultaneously improve accuracy and reduce the repetition rate. Firstly, a multimodal feature fusion algorithm with PSO adaptive weights is proposed. The emotional features of e-commerce are extracted by using Bi-GRU combined with attention, and the emotional features of images are extracted by using convolutional neural networks combined with attention. The shared semantic layer is studied. When conducting information fusion in the feature layer, the idea of particle swarm optimization is introduced. The multimodal emotional features are weighted and fused, and the feature vectors that have been weighted and fused through particle swarm optimization are taken as the overall emotional vector. The sentiment vector was calculated for similarity through the explicit and implicit sentiment calculation formula. Then, the collaborative filtering model based on features (integrating diversity constraints) was studied. New products were represented as feature quantities, and the score was predicted by calculating the similarity of product features. Experiments show that this method can effectively solve the problems of data sparsity, cold start of new products and high repetition rate of recommendations.