An effective knowledge-based recommendation system for supply chain information based on UserCF algorithm

This research paper illustrates the knowledge-based system for supply chain information as the application platform. The algorithm uses the latent Dirichlet allocation (LDA) model to mine knowledge's semantic connotation and characterise user interests. Additionally, fuzzy C-means (FCM) algorithm limits the traversal range to cluster users. An adapted function is optimised and focused on previous literature reviews and research gaps. The optimised adapted function is applied to the knowledge sharing and dissemination platform Scopus index. Firstly, user knowledge documents are obtained, and the topic-optimised LDA model is used to mine user knowledge topics. Then, users are clustered through the FCM algorithm to reduce the traversal range of the similarity calculation, and JS divergence is used instead of Euclidean distance to realise the conversion from the FCM object to the user. Finally, based on the UserCF algorithm, user's interest index in knowledge is constructed, and the TOP-N recommendation is made.