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Embedding Method for Sufficiently Expressing Business Behavior Characteristics

In order to realize intelligent operation and maintenance, it is necessary to convert the business behavior log into a digital form that can be processed by the intelligent decision model. For the embedding representation of business behavior characteristics, data fusion based on behavior type and sparse embedding representation based on discrete normalization are given, and the input data of business behavior is vectorized into 152-bit sparse vector. Combined with the advantages of knowledge graph, data fusion and dense embedding representation based on knowledge graph are proposed, and the input data is vectorized into a dense vector represented by 151 real numbers. As can be seen from the embedding results, the scale of one-dimensional vector obtained by the two embedding methods is approximate, and compared with the sparse integer vector, the dense vector can more sufficiently express business behavior characteristics and are more suitable for deep learning.