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Method for predicting popularity of shop based on singular value decomposition

A technology of singular value decomposition and popularity, applied in forecasting, marketing, data processing applications, etc., can solve problems such as low efficiency, and achieve the effect of improving efficiency

Active Publication Date: 2015-09-09
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

[0003] In order to overcome the inefficiency of existing store location selection systems and methods, the present invention provides a store popularity prediction method based on singular value decomposition

Method used

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  • Method for predicting popularity of shop based on singular value decomposition
  • Method for predicting popularity of shop based on singular value decomposition
  • Method for predicting popularity of shop based on singular value decomposition

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Embodiment Construction

[0027] refer to figure 1 . The specific steps of the shop popularity prediction method based on singular value decomposition of the present invention are as follows:

[0028] 1. Store data capture.

[0029] Use web crawlers to grab all the store data in Shanghai, and combine the information provided by location-based services (latitude and longitude of the location, traffic information) to complete the store information. Organize the store data into a triplet format of , and divide all the data into training data and test data in a ratio of 8:2.

[0030] 2. Store feature extraction and quantification.

[0031] The features extracted from the store information are as follows:

[0032] The distance from the center of the business district where it is located, define F s =logD s , where D s is the distance from the store s to the center of the business district where it is located, F s Indicates the distance between the shop and the commercial area.

[0033] Accessibilit...

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Abstract

The invention discloses a method for predicting the popularity of a shop based on singular value decomposition (SVD) and solves a technical problem of low efficiency of a conventional shop address selecting system and method. The method uses user preference as a medium, adds shop characteristic fusion and shop preference learning on the basis of SVD, and acquires effective information by using social media and location-based service, wherein the information includes the information of commercial areas around the shop, traffic information around the shop, and passenger flow information around the shop. The method for predicting the popularity by using the SVD not only embodies recessive characters but also embodies extracted dominant characters. In a matrix decomposition process, a left singular vector, a right singular vector, and a shop feature vector are iteratively computed. A neighbor shop of a newly-opened shop is computed by using the shop, the vector parameter value of the newly-opened shop is acquired by fitting. The method solves a problem of inaccurate newly-opened shop parameter due to matrix sparesity and is improved in efficiency.

Description

technical field [0001] The invention relates to a shop popularity prediction method, in particular to a shop popularity prediction method based on singular value decomposition. Background technique [0002] The document "Store Location Selection System and Method" discloses a store location selection system and method. This method determines the scope of the business district through the format of the new store, and then determines the existing stores and residential areas, and finds out the attractiveness of these stores. Factor information, using regression analysis, analyzes the relationship between sales and various influencing factors, and determines the The degree of influence of factors on attractiveness is further calculated by using the multi-factor attractiveness model to calculate the attractiveness of new stores through the obtained influencing factors and their corresponding adjustment indexes. Although the method described in the literature starts from the per...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02
Inventor 於志文田苗郭斌王柱周兴社
Owner NORTHWESTERN POLYTECHNICAL UNIV
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