Commodity brand feature acquisition method and device, sales volume prediction method and device and electronic equipment

Abstract

The invention relates to a commodity brand feature acquisition method and device, a sales volume prediction method and device and electronic equipment, and belongs to the technical field of artificialintelligence. The method comprises the steps of: obtaining historical daily average sales volume sequences corresponding to multiple commodity brands including a target commodity brand; converting the historical daily average sales sequence corresponding to each commodity brand into a sentence character string; forming a matrix array based on all the sentence character strings, wherein each row in the matrix array corresponds to one sentence character string; converting each sentence character string in the matrix array into a corresponding sentence vector based on a word vector model to obtain a semantic vector matrix; and clustering each sentence vector in the semantic vector matrix to obtain a label corresponding to the class where the clustered target commodity brand is located, the label being a brand feature corresponding to the target commodity brand. A high-dimensional sparse feature matrix brought by adopting one-hot coding is avoided, so that the time and space required fortraining the sales prediction model are reduced, and the prediction precision of the sales prediction model is improved.

Description

technical field [0001] The present application belongs to the technical field of artificial intelligence, and specifically relates to a method for acquiring commodity brand features, a sales forecast method, a device, and electronic equipment. Background technique [0002] For shopping malls and supermarkets, accurate commodity sales forecasts can help them formulate supply and replenishment strategies that maximize profits, thereby increasing turnover rates and reducing out-of-stock rates. In the industrial world, it is generally possible to construct features from the location of the store, customer flow, and product-related attributes. Specifically, it is generally a sliding window of different periods for numerical features, and one-hot encoding for category features, and then use the ensemble tree model to Forecast the future sales of goods. [0003] However, there are a large number of discrete features related to product attributes, such as product types and brand in...

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Problems solved by technology

[0004] In view of this, the purpose of this application is to provide a product brand feature acquisition method, sales forecast method, device, and electronic equipment to improve the high-dimensional sparseness caused by the current use of one-hot encoding to obtain discrete features related to product attributes. The feature matrix, which leads to the problem of additional time overhead when training the sales forecast model

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