Design of a Social Network Data Deep Learning Recommendation System Guided by User Preference

The currently designed social network data deep learning recommendation system has a low matching degree of recommendation information and poor user satisfaction. To solve the above problems, a new social network data deep learning recommendation system is designed under the guidance of user preferences, and the hardware and software of the system are optimized. The system hardware is mainly composed of an information collector, information storage analyzer, recommender, data communicator, clock chip, sensor and memory. Through data analysis, measuring diversity and matching, and calculating perceived time, we complete the recommendation of in-depth learning. The experimental results show that the social network data deep learning recommendation system designed under the guidance of user preference has good information matching ability, can improve user satisfaction and reduce recommendation error.