Shop optimization addressing method based on transfer learning

A transfer learning and optimization technology, applied in machine learning, business, instrumentation, etc., can solve the problem that the model cannot achieve good results, and achieve the effect of improving scalability

Inactive Publication Date: 2017-07-14
ZHEJIANG UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, in some areas of the city or for some small cities, some data such as social media and other data are

Method used

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  • Shop optimization addressing method based on transfer learning
  • Shop optimization addressing method based on transfer learning
  • Shop optimization addressing method based on transfer learning

Examples

Experimental program
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Embodiment 1

[0056] In this embodiment, Beijing, Shanghai, Guangzhou, Wuhan, and Shenzhen are used as source cities, and Hangzhou is used as a target city for site selection. According to the situation of Hangzhou's road network, Hangzhou is divided into 4315 areas, and each area is a candidate area for site selection.

[0057] The social media data is Weibo and Dianping texts, and the physical sensor data is traffic, bus, real estate prices, points of interest, and business circle data.

[0058] According to the urban planning data, the features of the construction rules of the construction of subway stations, demolition and planning of new commercial circles in each area of ​​Hangzhou are selected, and the relative entropy threshold is set at 0.1.

[0059] Analyze the characteristics of the cities of Beijing, Shanghai, Guangzhou, Wuhan, and Shenzhen and the data characteristics of Hangzhou to obey different distributions, so the source city and target city data are mapped to the intermed...

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Abstract

The present invention discloses a shop optimization addressing method based on transfer learning. The method comprises: dividing areas according to an urban road network; employing social media in each area and sensor data to construct features, employing data such as urban planning to construct rule features, multiplying factors such as a distance by the corresponding weight, and employing multi-view learning to perform feature fusion. The transfer learning is employed to transfer large-city addressing knowledge with rich data bulk to a relatively small city to train the optimization shop addressing model of the small city. The rule features and the trained model are fused to obtain a final optimal addressing model. The multi-view learning is configured to fuse different views of data to effectively extend the data of other sources, and based on the transfer learning technology, small cities having small samples also can obtain good effects.

Description

technical field [0001] The invention belongs to the fields of data mining and urban computing, and in particular relates to a method for optimal location selection of shops based on transfer learning. Background technique [0002] The optimal location of shops can bring strong economic benefits. An optimized location usually attracts more customers than a random location. For example, a new coffee shop can be opened near the intersection of roads. Usually, the intersection has convenient traffic and better passenger flow. However, factors such as traffic congestion may also have a negative impact on this location. With the continuous development of urban computing, it is possible to use the massive data in the city to optimize the location of shops. Traditional optimal location selection usually uses the characteristics of an area itself, such as the flow of people, purchasing power, traffic, etc., to construct a model. However, people do not necessarily stay in certain s...

Claims

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Application Information

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IPC IPC(8): G06Q30/00G06Q10/04G06N99/00
CPCG06Q30/00G06N20/00G06Q10/04
Inventor 陈华钧张宁豫陈曦吴朝晖
Owner ZHEJIANG UNIV
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