To improve product recommendation network, this paper mainly proposes a product similarity calculation algorithm based on deep confidence network. A high-dimensional product is firstly constructed and then input into the DBN model to obtain low-dimensional product feature data. Founded on the low-dimensional product feature data, the similarity between products can be calculated by the cosine formula. Through the data experiment, it is found that as the output dimension of the low-dimensional product feature matrix decreases, the similarity of the product similarity matrix also decreases, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing.
Abstract. In the area of recommendation system, clustering is an effective way to find similar users and reduce the complexity of recommendation algorithm. A good clustering result has two characteristics: maximizing compactness within cluster and maximizing separation between clusters. Following laws of attraction and repulsion, electric charges in an electric potential field have the same feature as clusters. Based on potential field, an improved Fuzzy C-Means (FCM) clustering algorithm is proposed, which is called EFCM (Electrical FCM). This algorithm combines the basic electrical rules of potential field, Coulomb's law and data field theory to obtain better clustering results. The experiment results show that the improved EFCM algorithm not only obtains good initial cluster centers, but also helps to improve the process of iterative updating.
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