1. A method for analyzing correlation between commodity webpage capacity and customer perception, the method comprising:

acquiring internet behavior data of the E-commerce platform from the DPI system;

extracting E-commerce platform webpage data and customer interaction perception data from the internet behavior data;

acquiring the commodity page capacity in a set period according to the commodity page data in the E-commerce platform webpage data;

according to the commodity page data and the customer interaction perception data, acquiring the attention frequency of each commodity in a set period;

acquiring the customer perception of each commodity according to the capacity of each commodity page in a set period and the attention frequency of each commodity in the set period;

and setting a commodity page capacity threshold according to the commodity customer perception.

2. The method of claim 1, wherein:

the E-commerce platform webpage data comprise URLs of all commodity pages, embedded URLs of the commodity pages, the sizes of the URLs of the commodity pages and the sizes of the embedded URLs of the commodity pages;

the customer interaction perception data comprises customer basic information, time information, URL of a commodity page browsed by a customer and URL of a shopping cart.

3. The method according to claim 2, wherein the method for acquiring the page capacity of the commodity in the set period specifically comprises:

in a set period, obtaining the average value of the commodity page and the total value of the embedded URL of the commodity page according to the size of the URL of the commodity page in the webpage data of the E-commerce platform and the total value of the embedded URL of the commodity page;

the average value of the total values is the capacity of the commodity page in the period and is marked as A.

4. The method according to claim 3, wherein the method for obtaining the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data specifically comprises:

and acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

5. The method according to claim 4, wherein the method for obtaining the customer perception of each commodity according to the capacity of each commodity page in the set period and the attention frequency of each commodity in the set period specifically comprises:

acquiring the sum of the size of the URL of the commodity page and the click frequency of the URL of the commodity, of which the total value of the size of the embedded URL of the commodity page is smaller than A, put into a shopping cart;

acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A;

the customer perception of the commodity is the ratio of the sum of the click frequencies of the URLs of the commodities put into the shopping cart to the sum of the click frequencies of the URLs of the commodity pages.

6. An apparatus for analyzing correlation between commodity web page capacity and customer perception, the apparatus comprising:

the internet behavior data acquisition unit is used for acquiring internet behavior data of the e-commerce platform from the DPI system;

the data extraction unit is used for extracting E-commerce platform webpage data and customer interaction perception data from the Internet behavior data acquired by the Internet behavior data acquisition unit;

the commodity webpage capacity acquiring unit is used for acquiring the commodity webpage capacity in a set period according to the commodity webpage data in the E-commerce platform webpage data;

the customer perception information acquisition unit is used for acquiring the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data;

the commodity page capacity threshold setting unit is used for acquiring the customer perception of each commodity according to the size of each commodity page in a set period and the attention frequency of each commodity in the set period; and setting a commodity page capacity threshold according to the commodity customer perception.

7. The apparatus of claim 6, wherein the data extraction unit further comprises:

the E-commerce platform webpage data extraction module is used for extracting the URL of each commodity page, the embedded URL of each commodity page, the size of the URL of each commodity page and the size of the embedded URL of each commodity page, which are contained in the E-commerce platform webpage data, according to the internet surfing behavior data of the E-commerce platform;

and the customer interaction perception data extraction module is used for extracting customer basic information, time information, URL of a customer browsing commodity page and URL of a shopping cart contained in the customer interaction perception data according to the internet surfing behavior data of the E-commerce platform.

8. The apparatus according to claim 7, wherein the method for acquiring the commodity page capacity by the commodity page capacity acquiring unit is specifically:

in a set period, obtaining the average value of the commodity page and the total value of the embedded URL of the commodity page according to the size of the URL of the commodity page in the webpage data of the E-commerce platform and the total value of the embedded URL of the commodity page;

the average value of the total values is the capacity of the commodity page in the period and is marked as A.

9. The apparatus according to claim 8, wherein the method for the customer perception information obtaining unit to obtain the frequency of interest of each commodity in the set period is specifically:

and acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

10. The apparatus of claim 9, wherein the merchandise page capacity threshold setting unit further comprises:

a first calculation module: the sum of the click frequency of the URL of the commodity page and the total value of the size of the embedded URL of the commodity page is less than A; acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A;

the second calculation module is used for acquiring the ratio of the sum of the click frequencies of the URLs of the commodities put into the shopping cart to the sum of the click frequencies of the URLs of the commodity pages, and taking the ratio as the customer perception of the commodities;

and the threshold setting module is used for setting the customer perception of the commodity as a commodity page capacity threshold.

Background

Shopping on e-commerce platforms has become one of people's daily activities. People browse the commodity page and decide whether to buy the commodity. In order to increase sales, the merchant displays all the characteristics of the commodity on the commodity page as much as possible. In order to show the commodity, a merchant needs to load a plurality of commodity pictures on a commodity page, and the commodity page is large due to the excessive commodity pictures, so that the commodity page is displayed in a browser of people for too long time, and people can abandon the purchase of the commodity. Therefore, obtaining the relationship between the size of the commodity page and the purchasing behavior of people becomes the key for designing the reasonable size of the commodity page.

Although the existing technical scheme focuses on the customer behavior data, the interest of customers in commodities is predicted based on the customer behavior data, and related advertisements are pushed. There is no technical scheme for analyzing the customer behavior data and the commodity page size data in a correlated manner.

Therefore, a technology capable of assisting E-commerce website developers to develop reasonable commodity pages according to customer perception and improving E-commerce commodity sales success rate is in urgent need.

Disclosure of Invention

The invention aims to provide a method for determining the page capacity of E-commerce commodities according to the internet behaviors of customers, so as to achieve the technical means of displaying the commodities with reasonable page capacity and improving the commodity sales success rate.

In order to achieve the above object, the present invention provides a method for analyzing correlation between commodity page capacity and customer perception, the method comprising:

acquiring internet behavior data of the E-commerce platform from the DPI system;

extracting E-commerce platform webpage data and customer interaction perception data from the internet behavior data;

acquiring the commodity page capacity in a set period according to the commodity page data in the E-commerce platform webpage data;

according to the commodity page data and the customer interaction perception data, acquiring the attention frequency of each commodity in a set period;

acquiring the customer perception of each commodity according to the capacity of each commodity page in a set period and the attention frequency of each commodity in the set period;

and setting a commodity page capacity threshold according to the commodity customer perception.

Specifically, the method comprises the following steps:

the E-commerce platform webpage data comprise URLs of all commodity pages, embedded URLs of the commodity pages, the sizes of the URLs of the commodity pages and the sizes of the embedded URLs of the commodity pages;

the customer interaction perception data comprises customer basic information, time information, URL of a commodity page browsed by a customer and URL of a shopping cart.

More specifically:

in a set period, obtaining the average value of the commodity page and the total value of the embedded URL of the commodity page according to the size of the URL of the commodity page in the webpage data of the E-commerce platform and the total value of the embedded URL of the commodity page;

the average value of the total values is the capacity of the commodity page in the period and is marked as A.

Further:

and acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

Further, the method comprises the following steps:

acquiring the sum of the size of the URL of the commodity page and the click frequency of the URL of the commodity, of which the total value of the size of the embedded URL of the commodity page is smaller than A, put into a shopping cart;

acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A;

the customer perception of the commodity is the ratio of the sum of the click frequencies of the URLs of the commodities put into the shopping cart to the sum of the click frequencies of the URLs of the commodity pages.

The invention also provides a commodity webpage capacity and customer perception correlation analysis device, which is characterized by comprising:

the internet behavior data acquisition unit is used for acquiring internet behavior data of the e-commerce platform from the DPI system;

the data extraction unit is used for extracting E-commerce platform webpage data and customer interaction perception data from the Internet behavior data acquired by the Internet behavior data acquisition unit;

the commodity webpage capacity acquiring unit is used for acquiring the commodity webpage capacity in a set period according to the commodity webpage data in the E-commerce platform webpage data;

the customer perception information acquisition unit is used for acquiring the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data;

the commodity page capacity threshold setting unit is used for acquiring the customer perception of each commodity according to the size of each commodity page in a set period and the attention frequency of each commodity in the set period; and setting a commodity page capacity threshold according to the commodity customer perception.

The data extraction unit further includes:

the E-commerce platform webpage data extraction module is used for extracting the URL of each commodity page, the embedded URL of each commodity page, the size of the URL of each commodity page and the size of the embedded URL of each commodity page, which are contained in the E-commerce platform webpage data, according to the internet surfing behavior data of the E-commerce platform;

and the customer interaction perception data extraction module is used for extracting customer basic information, time information, URL of a customer browsing commodity page and URL of a shopping cart contained in the customer interaction perception data according to the internet surfing behavior data of the E-commerce platform.

Specifically, the method for acquiring the page capacity of the commodity by the commodity page capacity acquisition unit specifically includes:

in a set period, obtaining the average value of the commodity page and the total value of the embedded URL of the commodity page according to the size of the URL of the commodity page in the webpage data of the E-commerce platform and the total value of the embedded URL of the commodity page;

the average value of the total values is the capacity of the commodity page in the period and is marked as A.

Further, the method for acquiring the attention frequency of each commodity in the set period by the customer perception information acquisition unit specifically includes:

and acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

Specifically, the commodity page capacity threshold setting unit further includes:

a first calculation module: the sum of the click frequency of the URL of the commodity page and the total value of the size of the embedded URL of the commodity page is less than A; acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A;

the second calculation module is used for acquiring the ratio of the sum of the click frequencies of the URLs of the commodities put into the shopping cart to the sum of the click frequencies of the URLs of the commodity pages, and taking the ratio as the customer perception of the commodities;

and the threshold setting module is used for setting the customer perception of the commodity as a commodity page capacity threshold.

The invention discloses a commodity webpage capacity and customer perception correlation analysis method and device, which measure customer perception by using the button rate of clicking a 'putting in a shopping cart' button by a customer, analyze the relation between the commodity size of an E-commerce webpage and the customer perception, determine the optimal E-commerce commodity webpage size and improve the selling success rate of the E-commerce commodity.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a method for analyzing correlation between commodity webpage capacity and customer perception according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of a method provided in a second embodiment of the present application;

fig. 3 is a schematic structural diagram of an apparatus for correlation analysis of commodity webpage capacity and customer perception according to a fourth embodiment of the present application;

fig. 4 is a schematic structural diagram provided in the fifth embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

As shown in fig. 1, it is a flowchart of a method for analyzing correlation between commodity page capacity and customer perception, and the embodiment includes the following steps:

step S01: and acquiring the internet surfing behavior data of the E-commerce platform from the DPI system.

DPI is a deep detection technology based on data packets, and can carry out deep detection aiming at different network application layer loads to obtain internet behavior data. Such as jump source, button address, dwell time, page size, user identification, etc.

Step S02: and extracting E-commerce platform webpage data and customer interaction perception data from the internet behavior data.

The E-commerce platform webpage data comprise URLs of all commodity pages, embedded URLs of the commodity pages, the sizes of the URLs of the commodity pages and the sizes of the embedded URLs of the commodity pages.

It should be noted that the URL of the goods page of different e-commerce may be different, but all of them may include information such as e-commerce address and goods identification. The embedded URL of the commodity page comprises the URL of the embedded webpage and the URL of the embedded picture; and the size of these URLs.

The customer interaction perception data comprises customer basic information, time information, URL of a commodity page browsed by a customer and URL of a shopping cart.

The client basic information is information that can identify the client, such as a client identifier, a client type, and the like, and generally may be a mobile phone number, a user name, and the like.

Since the URLs of the e-commerce are different, the commodity identification and the e-commerce platform to which the commodity identification belongs can be extracted from the URLs.

Step S03: and acquiring the commodity page capacity in a set period according to the commodity page data in the E-commerce platform webpage data.

The commodity page capacity generally refers to the total size of the commodity web page and its embedded URL. In order to show the commodity, the merchant needs to load a plurality of commodity pictures on the webpage, and the commodity webpage with the excessive commodity pictures is too large, so that the browsing time of the customer is increased, and the customer can abandon the purchase of the commodity. Therefore, the page capacity of the commodity and the purchasing behavior of the customer have a necessary relation.

Step S04: and acquiring the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data.

And obtaining the attention frequency of each commodity through the click times of the customer to the commodity page URL and the URL put into the shopping cart in a certain period.

The period may be set by actual conditions, and may be set to one week, one month, or one year, or may be set in units of hours and minutes by modifying the period according to actual conditions.

Step S05: and acquiring the customer perception of the commodities according to the capacity of each commodity page in a set period and the attention frequency of each commodity in the set period.

And acquiring the relationship between the page capacity of the commodity and the attention frequency of the commodity so as to further acquire the customer perception of the commodity.

Step S06: and setting a commodity page capacity threshold according to the commodity customer perception.

According to the invention, the customer perception is measured by using the button rate of clicking the 'putting in a shopping cart' by the customer, the relation between the E-commerce webpage commodity size and the customer perception is analyzed, the optimal E-commerce commodity webpage size is determined, and the selling success rate of the E-commerce commodity is improved.

In order to better illustrate the present invention, a second embodiment is given to explain the working principle of each step in detail, as shown in fig. 2.

Step S201: and acquiring the internet surfing behavior data of the E-commerce platform from the DPI system.

Step S202: and extracting E-commerce platform webpage data and customer interaction perception data from the internet behavior data.

The E-commerce platform webpage data comprise URLs of all commodity pages, embedded URLs of the commodity pages, the sizes of the URLs of the commodity pages and the sizes of the embedded URLs of the commodity pages;

the customer interaction perception data comprises customer basic information, time information, URL of a commodity page browsed by a customer and URL of a shopping cart. As shown in tables 1, 2, 3 and 4.

TABLE 1

Commodity webpage URL	Embedded URL

TABLE 2

URL	Size (kb)

TABLE 3

Mobile phone number	Time	Commodity webpage URL

TABLE 4

Mobile phone number	Time	"put shopping cart" button URL

The item page URLs for different e-commerce may vary.

The URL of a commodity page of a merchant AA is https:// item.AA.XXX/YYYYY.html, XXX and YYYYYYYY can be any number and letter, and YYYYYYYY represents a commodity identifier;

the URL of the commodity page of the merchant BB is http:// product.BB.com/YYYYYY.html, YYYYYYYY can be any number and letter, and YYYYYY represents the commodity identification. The present invention does not involve analyzing commodity URL features of individual e-commerce web sites.

The embedded URL of the commodity webpage comprises the URL of the embedded webpage and the picture URL.

The put-in shopping cart button URL may vary from e-commerce to e-commerce.

The merchant AA is:

https:// cart.aa.com/addtocart.html pid ═ YYYY & … …, YYYYY can be any number and letter, YYYY stands for commodity identification.

The merchant BB is:

http { "product" { "product id": YYYYY, … …, yyyyyy is any number and letter, and YYYYY represents a commodity identification.

The URL characteristics of all merchants can be identified according to actual conditions, and the method does not relate to analyzing the URL characteristics of shopping cart buttons of all E-commerce websites.

Step S203: and in a set period, obtaining the average value of the commodity page and the total value of the embedded URL of the commodity page according to the size of the URL of the commodity page in the webpage data of the E-commerce platform and the total value of the embedded URL of the commodity page.

The average value of the total values is the capacity of the commodity page in the period and is marked as A.

And extracting the commodity identification and the electronic commerce platform to which the commodity identification belongs from the URL of the commodity webpage, and periodically (for example, every month) calculating the total size of each commodity page and the embedded URL thereof. Generally, a commodity web page URL is clicked many times, and at this time, the average value of the total size of the commodity web page and the embedded URL thereof is calculated, as shown in table 5:

TABLE 5 Total Web Page size mean

E-commerce platform	Commodity webpage URL	Commodity identification	Counting the start time	End time of statistics	Average size of web page (kb)

Step S204: and acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

Extracting a commodity identification and an affiliated e-commerce platform thereof from a commodity webpage URL and a 'put in shopping cart' button URL; periodically (e.g., every month), the "put shopping cart" button URL clicks for each item are counted, and the item web page URL clicks are shown in table 6.

TABLE 6 frequency of interest acquisition

Step S205: and acquiring the sum of the size of the URL of the commodity page and the click frequency of the URL of the commodity, of which the total value of the size of the embedded URL of the commodity page is less than A, put into the shopping cart.

The sum of the URL clicking frequencies of the commodities with the total size value smaller than A when the commodities are put into the shopping cart is as follows: SUM (URL click times of "put shopping cart" button of web page less than or equal to a), SUM represents accumulation.

Step S206: and acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A.

The sum of the click frequencies of the URL of the commodity page of the commodity with the total size value smaller than A is as follows: SUM (number of product web page URL clicks of web pages equal to or smaller than a), which indicates accumulation.

Step S207: and determining the customer perception of the commodity as the ratio of the sum of the URL clicking frequencies of the commodity put into a shopping cart to the sum of the URL clicking frequencies of the commodity page.

The customer perception is SUM (URL clicking times of a 'put shopping cart' button of a webpage less than or equal to A)/SUM (URL clicking times of a commodity webpage of a webpage less than or equal to A), and the SUM represents accumulation.

Step S208: and setting a commodity page capacity threshold according to the commodity customer perception.

By the technical scheme, the internet behavior data of the customer can be conveniently obtained from the DPI system, the customer perception degree can be measured by clicking the button rate of putting the shopping cart by the customer, the relation between the size of the commodity webpage and the customer perception degree is analyzed, and the optimal webpage capacity of the E-commerce commodity is determined. The waste of the webpage capacity is avoided, and meanwhile, the success rate of commodity sales is improved.

In order to better illustrate the method of the present invention, a third embodiment of the present invention is given in combination with the actual situation.

The data of the internet surfing behavior of the customer on the e-commerce platform is collected from the DPI system, and is shown in tables 3-1 to 3-4:

table 3-1 collects internet behavior data

Table 3-2 collected internet behavior data

Table 3-3 collected internet behavior data

Mobile phone number	Time	Commodity webpage URL
			139aaaabbbb	20190324 14:41:00	https://item.AA.com/34280269098.html
……	……	……

Table 3-4 collected internet behavior data

And extracting the commodity identification and the electronic commerce platform to which the commodity identification belongs from the URL of the commodity webpage, and calculating the total size of each commodity page and the embedded URL thereof at the end of each month and month. Generally, a commodity webpage URL is clicked many times, and at this time, the average value of the total size of the commodity webpage and the embedded URL thereof is calculated. As shown in tables 3-5:

TABLE 3-5 Total Web Page size average

Extracting a commodity identification and an affiliated e-commerce platform thereof from a commodity webpage URL and a 'put in shopping cart' button URL; and counting the number of clicks of the URL of the button for putting in the shopping cart and the number of clicks of the URL of the webpage of the commodity of each commodity at the end of each month and month. As shown in tables 3-6:

tables 3-6 customer perception frequency

The data of the table 3-7 are obtained by the association analysis of 'E-commerce platform', 'commodity identification', 'statistical start time', 'statistical end time':

tables 3-7 Commodity Page information associated with customer perception

The latest statistical data in tables 3 to 7 are sorted according to the value of "average size of web page (kb)", and the click "put in shopping cart" button rate of each "average size of web page (kb)" (hereinafter, abbreviated as a) is counted:

the "shopping cart in" button click rate is SUM (number of clicks of URL of "shopping cart in" button of web page equal to or less than a)/SUM (number of clicks of URL of commodity web page of web page equal to or less than a).

SUM in the above equation represents accumulation.

In tables 3 to 7: the 1kb web page is the smallest web page in table 7, the number of clicks of the URL of the merchandise web page is 150, and the number of clicks of the URL of the "cart in" button is 145, and the rate of clicks of the "cart in" button of the 1kb web page is 145/150-96.7%.

The 1.5kb web page is the third web page in tables 3-7, the number of clicks of the URL of the commodity web page is 200, the number of clicks of the URL of the "put shopping cart" button is 190, and the click rate of the "put shopping cart" button of the 1.5kb web page can be calculated to be 95.7%

And so on … …

The customer perception degree is obtained by clicking the button of 'putting in a shopping cart'.

Tables 3-8 customer perception lists

Counting the start time	End time of statistics	Average size of web page (kb)	Click "put shopping cart" button rate
				20190301	20190331	1	96.7％
20190301	20190331	1.5	95.7％
				……	……	……	……

According to the data in tables 3-8, the developer of the E-commerce website can obtain the upper limit of the size of the webpage of the commodity according to the required button rate of clicking the 'putting in a shopping cart'.

The invention also discloses a commodity webpage capacity and customer perception correlation analysis device, and a fourth embodiment of the invention is provided, as shown in fig. 3, for explaining the structural characteristics of the device.

The device includes:

the internet behavior data acquisition unit 1 is used for acquiring internet behavior data of the e-commerce platform from the DPI system.

And the data extraction unit 2 is used for extracting E-commerce platform webpage data and customer interaction perception data from the Internet behavior data acquired by the Internet behavior data acquisition unit.

And the commodity webpage capacity acquiring unit 3 is used for acquiring the commodity webpage capacity in a set period according to the commodity webpage data in the E-commerce platform webpage data.

And the customer perception information acquisition unit 4 is used for acquiring the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data.

A commodity page capacity threshold setting unit 5, configured to obtain a customer perception of each commodity according to the size of each commodity page in a set period and the frequency of interest of each commodity in the set period; and setting a commodity page capacity threshold according to the commodity customer perception.

The patent refers to the field of 'electric digital data processing'. The device measures customer perception by using the button rate of clicking 'putting in a shopping cart' by a customer, and correlatively analyzes the relation between the webpage size of the E-commerce commodity and the customer perception to determine the optimal webpage size of the E-commerce commodity.

In order to better explain the structure and the working mode of each part of the device, a fifth embodiment of the invention is given, as shown in fig. 4.

Specifically, the sample acquiring unit 1 further includes:

the internet behavior data acquisition unit 1 is used for acquiring internet behavior data of the e-commerce platform from the DPI system.

The data extraction unit 2 further includes:

the e-commerce platform webpage data extraction module 21 is configured to extract, according to the internet access behavior data of the e-commerce platform, the URL of each commodity page, the embedded URL of each commodity page, the size of the URL of each commodity page, and the size of the embedded URL of each commodity page included in the e-commerce platform webpage data.

And the customer interaction sensing data extraction module 22 is used for extracting the customer basic information, the time information, the URL of the webpage of the customer browsed goods and the URL of the shopping cart according to the internet surfing behavior data of the E-commerce platform.

And the commodity webpage capacity acquiring unit 3 is used for acquiring the commodity webpage capacity in a set period according to the commodity webpage data in the E-commerce platform webpage data.

And the commodity webpage capacity acquiring unit acquires the average value of the commodity page and the embedded URL total value thereof according to the size of the commodity page URL in the E-commerce platform webpage data and the total value of the embedded URL of the commodity page in a set period.

The average value of the total values is the capacity of the commodity page in the period and is marked as A.

And the customer perception information acquisition unit 4 is used for acquiring the attention frequency of each commodity in a set period according to the commodity page data and the customer interaction perception data.

And acquiring the click frequency of the URL of the commodity webpage and the click frequency of the URL of the shopping cart in a set period by the customer according to the customer interaction perception data.

The product page capacity threshold setting unit 5 further includes:

the first calculation module 51: the sum of the click frequency of the URL of the commodity page and the total value of the size of the embedded URL of the commodity page is less than A; and acquiring the sum of the size of the URL of the commodity page and the commodity page URL clicking frequency of the commodity of which the total value of the size of the embedded URL of the commodity page is less than A.

And the second calculating module 52 is configured to obtain a ratio of the sum of the URL click frequencies of the commodity placed in the shopping cart to the sum of the URL click frequencies of the commodity page, and use the ratio as the customer perception of the commodity.

And a threshold setting module 53, configured to set the customer perception of the product as a product page capacity threshold.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the foregoing method may be referred to for the corresponding process in the above-described apparatus embodiment, and is not repeated herein.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be practiced in sequences other than those illustrated.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.