A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning

Abstract

The online ideological as well as the political teaching resource management system structure is established in the view of information management in colleges and universities. Furthermore, the online ideological as well as the political teaching information level is improved by combining the optimized design of resource recommendation model. In this paper, an online ideological as well as political teaching resource adaptive recommendation system and algorithm, which is designed on deep reinforcement learning, is suggested. The cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed. Similarly, the multidimensional constraint index parameter analysis method is adopted, and the adaptive matching model of online ideological as well as the political teaching resources is established. According to online ideological as well as the political teaching norms, combined with the analysis of high-quality educational resources of audience groups, the dynamic evaluation of online ideological as well as the political teaching resources and the adaptive matching model of interest preferences are established. Finally, the deep reinforcement learning method is adopted. By analyzing the characteristics of the resource structure model of online ideological as well as the political teaching resources, through benefit evaluation, resource supply and demand balance management analysis and balanced game control, the online ideological as well as the political teaching resources management system can be improved and self-adaptive recommended. The simulation outcomes indicate that this approach has noble adaptability and high correctness in recommending online ideological as well as the political teaching resources.