CN108093013B - Webpage data calculation method and server - Google Patents

Abstract

The embodiment of the invention discloses a webpage data calculation method, which is used for accurately evaluating the exit behavior of a user in a webpage and providing more effective data for webpage construction. The method provided by the embodiment of the invention comprises the following steps: the method comprises the steps that a server obtains a preset number of target sessions corresponding to a website, wherein each target session comprises one or more target pages; the server determines the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance degree; and the server calculates the exit rate of the target page according to the exit weight. The embodiment of the invention also discloses a server which is used for accurately evaluating the exit behavior of the user in the page and providing more effective data for webpage construction.

Description

A kind of web page data calculation method and server

technical field

The invention relates to the field of computer applications, in particular to a web page data calculation method and a server.

Background technique

In web page analysis, the page exit rate can evaluate the user experience effect of a certain page of a website, and is widely used in the fields of website optimization and website analysis.

Existing page exit rate, often defined as the percentage of times users exit a website divided by the number of times users enter a website.

However, the calculation of the exit rate of the webpage in the prior art is relatively simple, and the calculation process ignores many factors that affect the user's exit behavior. For example, the nature of pages, different pages on the same website have different levels of importance. For example, the order page is more important than the contact page. After the user visits some important pages, the corresponding possibility of exiting will increase. The calculation method of the existing technology ignores these factors. , the calculated exit rate cannot reasonably evaluate the user's exit behavior on the page.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a web page data calculation method and server, which are used to accurately evaluate a user's exit behavior in a web page and provide more effective data for web page construction.

An embodiment of the present invention provides a web page data calculation method, including:

The server obtains a preset number of target sessions corresponding to the website, and each target session includes one or more target pages;

The server determines the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance;

The server calculates the exit rate of the target page according to the exit weight.

Optionally, the server determines the exit weight corresponding to each target page in the preset number of target sessions according to the page browsing order and page importance, including:

For each target session, the server calculates the corresponding exit weight of each target page in the target session in the following manner:

(1-a) ^XY (1-b) ^Y ;

Wherein, the a is the first exit coefficient, the first exit coefficient is the exit probability coefficient corresponding to the preset general page, the general page is the page whose importance is general, and the b is the second exit coefficient, The second exit coefficient is a preset important page exit probability coefficient, the important page is a page with an important degree of importance, and the X is the total page views corresponding to the target page in the target session, so The Y is the page views of important pages in the total page views, and the X-Y is the page views of general pages in the total page traffic.

Optionally, calculating, by the server, the exit rate of the target page according to the exit weight includes:

The server calculates the exit rate GA by:

GA=M/N*100%;

Wherein, the M is the sum of the logout weights corresponding to the target pages in the target session as the logout pages, and the N is the sum of the logout weights corresponding to each target page in the target session.

Optionally, before the server acquires a preset number of target sessions, it includes:

The server divides each page in the website into a general page and an important page according to the importance of the content of the web page.

Optionally, the server acquiring a preset number of target sessions includes:

The server acquires a preset number of target sessions from historical session records corresponding to the website.

The embodiment of the present invention also provides a server, including:

an acquisition module, used to acquire a preset number of target sessions corresponding to the website, each target session contains one or more target pages;

A determination module, configured to determine the exit weight corresponding to each target page in the target session according to the page browsing order and page importance;

A calculation module, configured to calculate the exit rate of the target page according to the exit weight.

Optionally, the determining module includes:

The first calculation unit is configured to, for each target session, calculate the exit weight corresponding to each target page in the target session in the following manner:

(1-a) ^XY (1-b) ^Y ;

Wherein, the a is the first exit coefficient, the first exit coefficient is the exit probability coefficient corresponding to the preset general page, the general page is the page whose importance is general, and the b is the second exit coefficient, The second exit coefficient is a preset important page exit probability coefficient, the important page is a page with an important degree of importance, and the X is the total page views corresponding to the target page in the target session, so The Y is the page views of important pages in the total page views, and the X-Y is the page views of general pages in the total page traffic.

Optionally, the computing module includes:

The second calculation unit is used to calculate the exit rate GA in the following manner:

GA=M/N*100%;

Wherein, the M is the sum of the logout weights corresponding to the target pages in the target session as the logout pages, and the N is the sum of the logout weights corresponding to each target page in the target session.

Optionally, the server further includes:

The dividing module is used for dividing each page in the website into a general page and an important page according to the importance of the content of the web page.

Optionally, the obtaining module includes:

an acquiring unit, configured to acquire a preset number of target sessions from the historical session records corresponding to the website.

As can be seen from the above technical solutions, the embodiments of the present invention have the following advantages:

In the embodiment of the present invention, the server obtains a preset number of target sessions containing target pages, and determines the possibility of the target page exiting, that is, the exit weight, according to the order in which the target pages are browsed in the target session and the importance of the target pages. Then calculate the exit rate of this target page according to the exit weight. This solution can calculate the exit rate of the page based on the importance of the page itself and the access depth of the page in the session, so as to more accurately evaluate the user's exit behavior on the page and provide more effective data for web page construction.

Description of drawings

1 is a flowchart of an embodiment of a method for calculating web page data in an embodiment of the present invention;

FIG. 2 is a schematic diagram of an embodiment of a server in an embodiment of the present invention;

FIG. 3 is a schematic diagram of another embodiment of a server in an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments.

The terms "first", "second", "third", "fourth", etc. (if present) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to Describe a particular order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can, for example, be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

Embodiments of the present invention provide a web page data calculation method and server, which are used to accurately evaluate a user's exit behavior on a web page and provide more effective data for web page construction.

The following first introduces the web page data calculation method in the embodiment of the present invention. Please refer to FIG. 1. An embodiment of the web page data calculation method in the embodiment of the present invention includes:

101. The server obtains a preset number of target sessions corresponding to the website;

When the server wants to know the exit rate of the target page, the server obtains a preset number of target sessions corresponding to the website, where a session means that a user opens a browser, visits a certain website, and clicks multiple hypervisors on this website. Linking, that is, browsing multiple pages of this website, accessing multiple website resources on the server, and then closing the browser, the whole process is called a session. In the embodiment of the present invention, a target session refers to a session including one or more target pages, that is, a user visits the target page at least once during the target session.

102. The server determines the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance;

After acquiring the preset number of target sessions corresponding to the website, the server determines, for each target session, the exit weight corresponding to each target page in the target session according to the browsing order of each page in the target session and the importance of each page.

103. The server calculates the exit rate of the target page according to the exit weight.

After determining the exit weight corresponding to each target page in the preset number of target sessions, the server calculates the exit rate of the target page according to the exit weight.

In the embodiment of the present invention, the server obtains a preset number of target sessions containing target pages, and determines the possibility of the target page exiting, that is, the exit weight, according to the order in which the target pages are browsed in the target session and the importance of the target pages. Then calculate the exit rate of this target page according to the exit weight. This solution can calculate the exit rate of the page based on the importance of the page itself and the access depth of the page in the session, so as to more accurately evaluate the user's exit behavior on the page and provide more effective data for web page construction.

Based on the above-mentioned embodiment corresponding to FIG. 1 , in another embodiment of the webpage data calculation method provided by the embodiment of the present invention, the server divides the webpages in the website into general pages and important pages according to the importance of the webpages. The server may Determine the exit weight corresponding to each target page in the target session as follows:

For each target session, the server calculates the corresponding exit weight of each target page in the target session in the following way:

(1-a) ^XY (1-b) ^Y ;

Among them, a is a first exit coefficient, the first exit coefficient is a preset exit probability coefficient corresponding to a general page, and a general page is a page with a general degree of importance; b is a second exit coefficient, and the second exit coefficient is preset. The coefficient of exit probability of important pages, important pages are pages with an important degree of importance; X is the total page views corresponding to the target page in the target session; Y is the page views of important pages in the total page views; X-Y It is the number of pageviews of general pages in the total page traffic.

It should be noted that, in the embodiment of the present invention, a and b are between 0 and 1, and a is smaller than b. This setting is because the importance of the general page is lower than that of the important page, so the information obtained by the user after browsing the general page is less important, and it is likely to continue to visit other pages to obtain more important information. On the contrary, after browsing important pages, users may have obtained the information they want to know and exit the website. Therefore, the exit probability of general pages is less than that of important pages.

It should also be noted that, in the embodiment of the present invention, X is the total page views corresponding to the target page in the target session, which refers to the pages browsed by the visitor when the target page is browsed in the target session. The total number of , which can reflect the depth of visitor visits.

The embodiment of the present invention provides a way for the server to determine the exit weight corresponding to each target page in the target session, which improves the practicability of the solution.

Based on any one of the foregoing embodiments, in another embodiment of the webpage data calculation method provided by the embodiment of the present invention, the server may calculate the exit rate GA of the target page in the following manner:

GA=M/N*100%;

Wherein, M is the sum of the logout weights corresponding to the target pages serving as the logout pages in the target session, and N is the sum of the logout weights corresponding to each target page in the target session.

The embodiment of the present invention provides a specific way for the server to calculate the withdrawal rate, which improves the practicability of the solution.

Based on any one of the foregoing embodiments, in another embodiment of the web page data calculation method provided by the embodiment of the present invention, the server may perform the following steps before acquiring a preset number of target sessions:

The server divides each page in the website into a general page and an important page according to the importance of the page content.

It should be understood that, in addition to the importance of page content, the server may also divide pages into general pages and important pages according to other features such as page attributes.

The embodiments of the present invention provide a variety of ways for the server to divide general pages and important pages, thereby improving the flexibility of the solution.

Based on any one of the foregoing embodiments, in another embodiment of the web page data calculation method provided by the embodiment of the present invention, the server may acquire a preset number of target sessions in the following manner:

The server obtains a preset number of target sessions from the historical session records corresponding to the website.

It should be understood that, in addition to acquiring the target session from historical session records, the server may also acquire the target session from the session corresponding to the ongoing website, and may also acquire the target session through other means, which is not specifically limited here.

The embodiments of the present invention provide multiple ways for the server to acquire the target session, which improves the flexibility of the solution.

For ease of understanding, the web page data calculation method in the embodiment of the present invention is described below with a specific application scenario:

Website A includes pages W, F, N, E, and D. The server divides W and N into important pages and D, E, and F into general pages according to the importance of the content of the webpage. The server sets the exit probability coefficient (first exit coefficient) corresponding to general pages to 0.2, and the exit probability coefficient (second exit coefficient) corresponding to important pages to 0.3. Now the server wants to know the exit rate of page D (target page). First, the server obtains four (preset number) sessions from historical sessions, which are:

Session 1: W—F—N—F—W;

Session 2: N—W—E—W;

Session 3: F-N-N-D-E-W;

Session Four: N-W-E.

For the convenience of description, the W page browsed for the first time in session one is called W1, the W page browsed for the second time is called W2, the W page browsed for the first time in session two is called W3, and the second W page in session two is called W3. The W page browsed for the second time is called W4, the W page browsed in the third session is called W5, and the W page browsed in the fourth session is called W6. where W1, W4 and W5 are the exit pages of session one, two and three respectively

For W1, the total page views corresponding to W1 in session 1 is 1, that is, X=1; W1 is an important page, so Y=1, Y-X=0. Then the exit weight corresponding to W1 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ⁰ (1-0.3) ¹ = 0.7;

For W2, the total page views corresponding to W2 in session 1 is 5, that is, X=5; the page views of important pages are 3 (including two W and one N), so Y=3, Y-X= 2. Then the exit weight corresponding to W2 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ² (1-0.3) ³ = 0.21952;

For W3, the total page views corresponding to W3 in session 2 is 2, that is, X=2; the page views of important pages are 2 (including one W and one N), so Y=2, Y-X=0 . Then the exit weight corresponding to W3 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ⁰ (1-0.3) ² = 0.49;

For W4, the total page views corresponding to W4 in session 2 is 4, that is, X=4; the page views of important pages are 3 (including two W and one N), so Y=3, Y-X= 1. Then the exit weight corresponding to W4 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ¹ (1-0.3) ³ = 0.2744;

For W5, the total page views corresponding to W5 in session 3 is 6, that is, X=6; the page views of important pages are 3 (including one W and two N), so Y=3, Y-X= 3. Then the exit weight corresponding to W5 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ³ (1-0.3) ³ = 0.175616;

For W6, the total page views corresponding to W6 in session 4 is 2, that is, X=2; the page views of important pages are 2 (including one W and one N), so Y=2, Y-X=0 . Then the exit weight corresponding to W5 is:

(1-a) ^XY (1-b) ^Y = (1-0.2) ⁰ (1-0.3) ² =0.49.

For ease of understanding, please refer to Table 1 below:

After the server calculates the exit weights corresponding to the W pages (ie W1 to W6) in the sessions 1 to 4 (target sessions), it calculates the exit weights corresponding to the W (ie W2, W4 and W5) that are the exit pages in the sessions 1 to 4 The sum M=0.21952+0.2744+0.175616=0.669536, the sum of the exit weights corresponding to each W page (ie, W1 to W6) in sessions one to four sessions N=0.7+0.21952+0.49+0.2744+0.175616+0.49=2.349536, and finally Calculate the exit rate of page W GA=M/N*100%=0.669536/2.349536*100%=28.50%.

Taking session 2 as an example, when visiting page W (ie W3) for the first time, the visitor obtained a certain amount of information, so the exit weight of page W here is 0.49, and then session 2 visited page E and page again. W (ie W4), then after the page W visits this time, the visitor obtains more information than before, so the possibility of the visitor exiting will be higher. It should not be the same as the original exit rate, and the exit probability of both should be regarded as 1.

Taking session 3 and session 4 as examples, the number of pages visited before page W will also have an impact. Before the W page, session 4 obviously only obtained the information of N and W, while session 3 obtained the information of F, N, D, E and W. Obviously, the amount of information of session 3 is much larger, then when session 3 reaches When the page is W, the probability of its exit will also increase, so the exit weight should be reduced.

The web page data calculation method in the embodiment of the present invention is described above, and the server in the embodiment of the present invention is introduced below. Referring to FIG. 2, an embodiment of the server in the embodiment of the present invention includes:

an acquisition module 201, configured to acquire a preset number of target sessions corresponding to the website, and each target session includes one or more target pages;

A determination module 202, configured to determine the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance;

The calculation module 203 is configured to calculate the exit rate of the target page according to the exit weight.

In this embodiment of the present invention, the obtaining module 201 obtains a preset number of target sessions including the target page, and the determining module 202 determines the possibility of exiting the target page according to the order in which the target pages are browsed in the target session and the importance of the target page , that is, the exit weight, and the calculation module 203 calculates the exit rate of the target page according to the exit weight. This solution can calculate the exit rate of the page based on the importance of the page itself and the access depth of the page in the session, so as to more accurately evaluate the user's exit behavior on the page and provide more effective data for web page construction.

For ease of understanding, the server in the embodiment of the present invention is described in detail below, and please refer to FIG. 3 . Another embodiment of the server in the embodiment of the present invention includes:

an obtaining module 301, configured to obtain a preset number of target sessions corresponding to the website, and each target session includes one or more target pages;

A determination module 302, configured to determine the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance;

A calculation module 303, configured to calculate the exit rate of the target page according to the exit weight;

Wherein, the determining module 302 includes:

The first calculation unit 3021 is configured to, for each target session, calculate the corresponding exit weight of each target page in the target session in the following manner:

(1-a) ^XY (1-b) ^Y ;

Among them, a is a first exit coefficient, the first exit coefficient is a preset exit probability coefficient corresponding to a general page, a general page is a page whose importance is general, b is a second exit coefficient, and the second exit coefficient is a preset The exit probability coefficient of important pages, important pages are pages with an important degree of importance, X is the total page views corresponding to the target page in the target session, Y is the page views of important pages in the total page views, and X-Y is The number of page views in general out of total page traffic.

The calculation module 303 includes:

The second calculation unit 3031 is used to calculate the withdrawal rate GA in the following manner:

GA=M/N*100%;

Wherein, M is the sum of the logout weights corresponding to the target pages serving as the logout pages in the target session, and N is the sum of the logout weights corresponding to each target page in the target session.

Optionally, in this embodiment of the present invention, the server may further include:

The dividing module 304 is used for dividing each page in the website into a general page and an important page according to the importance of the content of the web page.

Optionally, in this embodiment of the present invention, the obtaining module 301 may include:

The obtaining unit 3011 is configured to obtain a preset number of target sessions from historical session records corresponding to the website.

In the embodiment of the present invention, the obtaining module 301 obtains a preset number of target sessions including the target page, and the determining module 302 determines the possibility of exiting the target page according to the order in which the target pages are browsed in the target session and the importance of the target page , that is, the exit weight, and the calculation module 303 calculates the exit rate of the target page according to the exit weight. This solution can calculate the exit rate of the page based on the importance of the page itself and the access depth of the page in the session, so as to more accurately evaluate the user's exit behavior on the page and provide more effective data for web page construction.

Secondly, the embodiment of the present invention provides a specific way for the determination module 302 to determine the exit weight, which improves the implementability of the solution.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above may refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full English name: Read-Only Memory, English abbreviation: ROM), random access memory (English full name: Random Access Memory, English abbreviation: RAM), disk or Various media that can store program codes, such as optical discs.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. a web page data calculation method, is characterized in that, comprises: The server obtains a preset number of target sessions corresponding to the website, and each target session includes one or more target pages; The server determines the exit weight corresponding to each target page in the target session according to the page browsing sequence and the page importance; The server calculates the exit rate of the target page according to the exit weight. 2. The method according to claim 1, wherein the server determines the exit weight corresponding to each target page in the preset number of target sessions according to the page browsing order and the page importance degree, comprising: For each target session, the server calculates the corresponding exit weight of each target page in the target session in the following manner: (1-a) ^XY (1-b) ^Y ; Wherein, the a is the first exit coefficient, the first exit coefficient is the exit probability coefficient corresponding to the preset general page, the general page is the page whose importance is general, and the b is the second exit coefficient, The second exit coefficient is a preset important page exit probability coefficient, the important page is a page with an important degree of importance, and the X is the total page views corresponding to the target page in the target session, so The Y is the page views of important pages in the total page views, and the X-Y is the page views of general pages in the total page traffic. 3. The method according to claim 1 or 2, wherein calculating, by the server, the exit rate of the target page according to the exit weight comprises: The server calculates the exit rate GA by: GA=M/N*100%; Wherein, the M is the sum of the logout weights corresponding to the target pages in the target session as the logout pages, and the N is the sum of the logout weights corresponding to each target page in the target session. 4. The method according to claim 1 or 2, wherein before the server acquires a preset number of target sessions, the method comprises: The server divides each page in the website into a general page and an important page according to the importance of the content of the web page. 5. The method according to claim 1 or 2, wherein the server acquiring a preset number of target sessions comprises: The server acquires a preset number of target sessions from historical session records corresponding to the website. 6. A server, characterized in that, comprising: an acquisition module, used to acquire a preset number of target sessions corresponding to the website, each target session includes one or more target pages; A determination module, configured to determine the exit weight corresponding to each target page in the target session according to the page browsing order and page importance; A calculation module, configured to calculate the exit rate of the target page according to the exit weight. 7. The server according to claim 6, wherein the determining module comprises: The first calculation unit is configured to, for each target session, calculate the exit weight corresponding to each target page in the target session in the following manner: (1-a) ^XY (1-b) ^Y ; Wherein, the a is a first exit coefficient, the first exit coefficient is a preset exit probability coefficient corresponding to a general page, the general page is a page with a general degree of importance, and the b is a second exit coefficient, The second exit coefficient is a preset important page exit probability coefficient, the important page is a page with an important degree of importance, and the X is the total page views corresponding to when the target page is browsed in the target session, so The Y is the page views of important pages in the total page views, and the X-Y is the page views of general pages in the total page traffic. 8. The server according to claim 6 or 7, wherein the computing module comprises: The second calculation unit is used to calculate the exit rate GA in the following manner: GA=M/N*100%; Wherein, the M is the sum of the logout weights corresponding to the target pages in the target session as the logout pages, and the N is the sum of the logout weights corresponding to each target page in the target session. 9. The server according to claim 6 or 7, wherein the server further comprises: The dividing module is used for dividing each page in the website into a general page and an important page according to the importance of the content of the web page. 10. The server according to claim 6 or 7, wherein the acquisition module comprises: an acquiring unit, configured to acquire a preset number of target sessions from the historical session records corresponding to the website.

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