Research on E-commerce Personalized Recommendation and Decision Support System Based on Deep Learning

Deep learning is an artificial intelligence technology that can be applied to various fields, and its application in e-commerce is becoming increasingly mature. Currently, personalized recommendation algorithms fail to construct incremental update models for recommendations, leading to delays in processing big data on e-commerce platforms. This results in outdated recommendation results, significantly affecting user experience. To address this issue, this paper proposes an automatic personalized information recommendation algorithm for e-commerce based on big data technology. First, the algorithm calculates the dissimilarity between users and items on the platform and constructs a data incremental update model using the computed dissimilarity matrix. Then, this model is utilized as a personalized recommendation model, incorporating the IU-UserCF recommendation algorithm to enhance prediction accuracy. Finally, the proposed method is applied to e-commerce logistics for further exploration. Experimental comparison results demonstrate that the proposed approach achieves superior recommendation performance in e-commerce applications.