Research on Personalized Resource Recommendation Algorithm Based on Computer Deep Learning

The resource recommendation is critical in intelligent personalized recommendation; however, it has an issue with erroneous performance positioning. The typical ant colony algorithm is unable to address the resource issue in intelligent personalized recommendation, and the result is insufficient. As a result, a computer deep learning-based research on personalized resource recommendation algorithm is provided, and the research on personalized resource recommendation algorithm is assessed. To begin, the neural network model theory is used to discover the influencing elements, and the indicators are split based on the resource recommendation’s needs to decrease interference factors in the resource recommendation. The neural network model theory is then used to create a computer deep learning resource recommendation scheme, and the outcomes of the resource recommendation are thoroughly examined. The MATLAB simulation results reveal that, under evaluation conditions, the computer deep learning outperforms the standard ant colony algorithm in terms of resource recommendation accuracy and time of influencing variables.