A knowledge recommendation algorithm based on tacit knowledge relation calculation

The rapid development of the Internet and the increase in the magnitude of the dissemination of knowledge and information have made it difficult for people to quickly find knowledge and information that suits them from the huge range of knowledge. Traditional knowledge recommendation algorithms often face many deficiencies in their use, and perform poorly in actual applications. This paper proposes a knowledge recommendation algorithm that calculates the correlation through the tacit knowledge relationship. It uses vector quantification of the implicit knowledge relationship, and uses the concept of information entropy to calculate the maximum recommendable range, uses expert samples to learn to assist decision-making, and adjusts the results through variable factors. Experiments show that the algorithm proposed in this paper can effectively improve the accuracy and efficiency of recommendation and ensure the diversity of recommendation results.