CN108093013B - Webpage data calculation method and server - Google Patents
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- CN108093013B CN108093013B CN201611042516.8A CN201611042516A CN108093013B CN 108093013 B CN108093013 B CN 108093013B CN 201611042516 A CN201611042516 A CN 201611042516A CN 108093013 B CN108093013 B CN 108093013B
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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
Technical Field
The invention relates to the field of computer application, in particular to a webpage data computing method and a server.
Background
In the webpage analysis, the webpage receding rate can evaluate the user experience effect of a certain page of a website, and is widely applied to the fields of website optimization and website analysis.
The conventional webpage exit rate is often defined as the percentage of the number of times that a user exits a website divided by the number of times that the user enters a browsing website.
However, in the prior art, the calculation of the webpage exit rate is simple, and many factors influencing the exit behavior of the user are ignored in the calculation process. For example, page properties, different pages on the same website have different importance levels, for example, an order page is more important than a contact information page, and after a user accesses a part of important pages, the possibility of corresponding logout is increased.
Disclosure of Invention
The embodiment of the invention provides a webpage data calculation method and a server, which are used for accurately evaluating the exit behavior of a user in a webpage and providing more effective data for webpage construction.
The embodiment of the invention provides a webpage data calculation method, which 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.
Optionally, the determining, by the server, 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 includes:
for each target session, the server calculates the exit weight of each target page in the target session by the following method:
(1-a)X-Y(1-b)Y；
the method comprises the steps that a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, the general page is a page with a general importance degree, 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, X is a total page browsing amount corresponding to the target page browsed in the target session, Y is a browsing amount of important pages in the total page browsing amount, and X-Y is a browsing amount of the general page in the total page flow.
Optionally, the calculating, by the server, the exit rate of the target page according to the exit weight includes:
the server calculates the withdrawal rate GA as follows:
GA＝M/N*100％；
the M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and the N is the sum of exit weights corresponding to each target page in the target session.
Optionally, before the obtaining of the preset number of target sessions, the server includes:
and the server divides each page in the website into a general page and an important page according to the importance degree of the webpage content.
Optionally, the obtaining, by the server, a preset number of target sessions includes:
and the server acquires a preset number of target sessions from the historical session records corresponding to the website.
An embodiment of the present invention further provides a server, including:
the acquisition module is used for acquiring a preset number of target sessions corresponding to the website, wherein each target session comprises one or more target pages;
the determining module is used for determining 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 calculation module is used for calculating the exit rate of the target page according to the exit weight.
Optionally, the determining module includes:
the first calculation unit is used for calculating the exit weight of each target page in each target session in the following way:
(1-a)X-Y(1-b)Y；
the method comprises the steps that a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, the general page is a page with a general importance degree, 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, X is a total page browsing amount corresponding to the target page browsed in the target session, Y is a browsing amount of important pages in the total page browsing amount, and X-Y is a browsing amount of the general page in the total page flow.
Optionally, the calculation module comprises:
a second calculation unit configured to calculate the withdrawal rate GA by:
GA＝M/N*100％；
the M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and the N is the sum of exit weights corresponding to each target page in the target session.
Optionally, the server further comprises:
and the dividing module is used for dividing each page in the website into a general page and an important page according to the importance degree of the webpage content.
Optionally, the obtaining module includes:
and the acquisition unit is used for acquiring preset number of target sessions from the historical session records corresponding to the websites.
According to the technical scheme, the embodiment of the invention has the following advantages:
in the embodiment of the invention, the server acquires a preset number of target sessions containing target pages, determines the possibility of target page quitting, namely quitting weight, according to the browsing sequence of the target pages in the target sessions and the importance degree of the target pages, and calculates the quitting rate of the target pages according to the quitting weight. According to the method and the device, the page quitting rate can be calculated by combining the importance degree of the page and the access depth of the page in the session, so that the quitting behavior of the user in the page can be evaluated more accurately, and more effective data can be provided for webpage construction.
Drawings
FIG. 1 is a flowchart of an embodiment of a method for calculating web page data according to an embodiment of the present invention;
FIG. 2 is a diagram of one embodiment of a server in an embodiment of the invention;
fig. 3 is a schematic diagram of another embodiment of the server in the embodiment of the present invention.
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.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention provides a webpage data calculation method and a server, which are used for accurately evaluating the exit behavior of a user in a webpage and providing more effective data for webpage construction.
Referring to fig. 1, a method for calculating web page data according to an embodiment of the present invention is described below, where an embodiment of the method for calculating web page data according to an embodiment of the present invention includes:
101. the server acquires 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, wherein the sessions refer to that a user opens a browser, accesses a certain website, clicks a plurality of hyperlinks on the website, namely, browses a plurality of pages of the website, accesses a plurality of website resources of the server, and then closes the browser, and the whole process is called as a session. The target session in the embodiment of the invention refers to a session containing one or more target pages, namely, the target page is accessed at least once by a user in the process of 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 degree;
after the server obtains a preset number of target sessions corresponding to the website, for each target session, determining exit weight corresponding to each target page in the target session according to the browsing sequence of each page in the target session and the importance degree of each page.
103. And the server calculates the exit rate of the target page according to the exit weight.
And 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 invention, the server acquires a preset number of target sessions containing target pages, determines the possibility of target page quitting, namely quitting weight, according to the browsing sequence of the target pages in the target sessions and the importance degree of the target pages, and calculates the quitting rate of the target pages according to the quitting weight. According to the method and the device, the page quitting rate can be calculated by combining the importance degree of the page and the access depth of the page in the session, so that the quitting behavior of the user in the page can be evaluated more accurately, and more effective data can be provided for webpage construction.
Based on the embodiment corresponding to fig. 1, in another embodiment of the method for calculating web page data provided in the embodiment of the present invention, the server divides the web pages in the website into two types, i.e., a general page and an important page, according to the importance degree of the web pages, and the server may determine the exit weight corresponding to each target page in the target session by the following method:
the server calculates the exit weight of each target page in each target session by the following method according to each target session:
(1-a)X-Y(1-b)Y；
wherein a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, and the general page is a page with a general degree of importance; b is a second exit coefficient which is a preset important page exit probability coefficient, and the important page is a page with important degree; x is the corresponding total page browsing amount when the target page is browsed in the target session; y is the browsing amount of the important pages in the total page browsing amount; and X-Y is the browsing amount of the general page in the total page flow.
In the embodiment of the invention, a and b are between 0 and 1, and a is smaller than b. The reason for this is that the importance of the general page is lower than that of the important page, and thus after the user browses the general page, the obtained information is less important, and it is likely that other pages will be continuously visited to obtain more important information. Conversely, after the user browses the important pages, the user is likely to obtain the information that the user wants to know, and thus quits the website. The exit probability of a general page is less than that of an important page.
It should be further noted that, in the embodiment of the present invention, X is a total page browsing amount corresponding to when the target page is browsed in the target session, where the total page browsing amount refers to a total number of pages browsed when the target page is browsed by a guest in the target session, and the total number can reflect a depth of access by the guest.
The embodiment of the invention provides a mode for determining the exit weight corresponding to each target page in the target session by the server, thereby improving the realizability of the scheme.
Based on any one of the foregoing embodiments, in another embodiment of the method for calculating web page data provided in the embodiment of the present invention, the server may calculate the exit rate GA of the target page by:
GA＝M/N*100％；
and M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and N is the sum of exit weights corresponding to each target page in the target session.
The embodiment of the invention provides a specific mode for calculating the withdrawal rate by the server, and the realizability of the scheme is improved.
Based on any one of the foregoing embodiments, in another embodiment of the method for calculating web page data provided in the embodiment of the present invention, before acquiring a preset number of target sessions, the server may perform the following steps:
the server divides each page in the website into a general page and an important page according to the importance degree of the page content.
It should be understood that in addition to the importance of the page content, the server may also divide the page into general pages and important pages according to other characteristics such as page attributes.
The embodiment of the invention provides a mode for dividing general pages and important pages by various servers, and improves the flexibility of the scheme.
Based on any one of the foregoing embodiments, in another embodiment of the method for calculating web page data provided in the embodiment of the present invention, the server may obtain a preset number of target sessions in the following manner:
the server acquires a preset number of target sessions from the historical session records corresponding to the websites.
It should be understood that, in addition to obtaining the target session from the historical session record, the server may also obtain the target session from a session corresponding to an ongoing website, and may also obtain the target session through other approaches, which is not limited herein.
The embodiment of the invention provides various modes for the server to acquire the target session, and improves the flexibility of the scheme.
For convenience of understanding, the following describes a web page data calculation method in an embodiment of the present invention in a specific application scenario:
the website A comprises pages W, F, N, E and D, and the server divides W and N into important pages according to the importance degree of the webpage content and divides D, E and F into general pages. The server sets the exit probability coefficient (first exit coefficient) corresponding to the general page to be 0.2, and sets the exit probability coefficient (second exit coefficient) corresponding to the important page to be 0.3. Now, when the server wants to know the drop rate of the page D (target page), the server first obtains four (preset number) sessions from the history session, which are:
conversation one: W-F-N-F-W;
and a second session: N-W-E-W;
and a third session: F-N-D-E-W;
and a fourth session: N-W-E.
For convenience of description, the first-viewed W page of the session is referred to as W1, the second-viewed W page is referred to as W2, the first-viewed W page of the session is referred to as W3, the second-viewed W page of the session is referred to as W4, the third-viewed W page is referred to as W5, and the fourth-viewed W page of the session is referred to as W6. Exit pages with W1, W4, and W5 being sessions one, two, and three, respectively
For W1, when browsing to W1 in session one, the corresponding total page browsing amount is 1, that is, X is 1; w1 is an important page, so Y is 1 and Y-X is 0. Then W1 corresponds to an exit weight of:
(1-a)X-Y(1-b)Y＝(1-0.2)0(1-0.3)1＝0.7；
for W2, when browsing to W2 in session one, the corresponding total page browsing amount is 5, that is, X is 5; the browsing amount of the important page is 3 (including two times of W and one time of N), so that Y is 3, and Y-X is 2. Then W2 corresponds to an exit weight of:
(1-a)X-Y(1-b)Y＝(1-0.2)2(1-0.3)3＝0.21952；
for W3, when browsing to W3 in session two, the corresponding total page browsing amount is 2, that is, X is 2; the browsing amount of the important page is 2 (including one time W and one time N), so that Y is 2, and Y-X is 0. Then W3 corresponds to an exit weight of:
(1-a)X-Y(1-b)Y＝(1-0.2)0(1-0.3)2＝0.49；
for W4, when browsing to W4 in session two, the corresponding total page browsing amount is 4, that is, X is 4; the browsing amount of the important page is 3 (including two times of W and one time of N), so that Y is 3, and Y-X is 1. Then W4 corresponds to an exit weight of:
(1-a)X-Y(1-b)Y＝(1-0.2)1(1-0.3)3＝0.2744；
for W5, when browsing to W5 in session three, the corresponding total page browsing amount is 6, that is, X is 6; the browsing amount of the important page is 3 (including one time of W and two times of N), so that Y is 3, and Y-X is 3. Then W5 corresponds to an exit weight of:
(1-a)X-Y(1-b)Y＝(1-0.2)3(1-0.3)3＝0.175616；
for W6, when browsing to W6 in session four, the corresponding total page browsing amount is 2, that is, X is 2; the browsing amount of the important page is 2 (including one time W and one time N), so that Y is 2, and Y-X is 0. Then W5 corresponds to an exit weight of:
(1-a)X-Y(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 weight corresponding to each W page (i.e., W1 to W6) in session one to session four (target sessions), the server calculates the exit weight sum M of the exit weights corresponding to W (i.e., W2, W4, and W5) as the exit page in session one to session four (target sessions) 0.21952+0.2744+ 0.175616-0.669536, the exit weight sum N of each W page (i.e., W1 to W6) in session one to session four is 0.7+0.21952+0.49+0.2744+0.175616+ 0.49-2.349536, and finally calculates the exit rate GA M/N100% of page W-0.669536/2.349536-28.50%.
Taking session two as an example, when page W (i.e., W3) is accessed for the first time, the visitor acquires a certain amount of information, so the exit weight of W page here is 0.49, and when page E and page W (i.e., W4) are accessed for the second following session, the visitor acquires more information after this access, and the possibility of the visitor exiting is higher. The exit probabilities of both should not be considered to be 1 as with the original exit rates.
Taking session three and session four as examples, the number of pages previously accessed by page W will also have an impact. Before the W page, the session four obviously only acquires the information of N and W, while the session three acquires the information of F, N, D, E and W, and obviously, the information amount of the session three is much larger, so when the session three reaches the W page, the exit probability is increased, and the exit weight should be reduced.
With reference to fig. 2, a server in an embodiment of the present invention is described below, where the method for calculating webpage data in the embodiment of the present invention is described above, and an embodiment of the server in the embodiment of the present invention includes:
an obtaining module 201, configured to obtain a preset number of target sessions corresponding to a website, where each target session includes one or more target pages;
a determining module 202, configured to determine, according to the page browsing order and the page importance level, an exit weight corresponding to each target page in the target session;
and the calculating module 203 is used for calculating the exit rate of the target page according to the exit weight.
In the embodiment of the present invention, the obtaining module 201 obtains a preset number of target sessions including target pages, the determining module 202 determines the possibility of exiting the target pages, i.e., exiting weight, according to the browsing sequence of the target pages in the target sessions and the importance degree of the target pages, and the calculating module 203 calculates the exiting rate of the target pages according to the exiting weight. According to the method and the device, the page quitting rate can be calculated by combining the importance degree of the page and the access depth of the page in the session, so that the quitting behavior of the user in the page can be evaluated more accurately, and more effective data can be provided for webpage construction.
For convenience of understanding, the following describes the server in the embodiment of the present invention in detail, and with reference 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 a website, where each target session includes one or more target pages;
a determining module 302, configured to determine, according to the page browsing order and the page importance level, an exit weight corresponding to each target page in the target session;
a calculating module 303, configured to calculate an exit rate of the target page according to the exit weight;
wherein the determining module 302 comprises:
a first calculating unit 3021, configured to calculate, for each target session, a corresponding exit weight of each target page in the target session by:
(1-a)X-Y(1-b)Y；
wherein a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, the general page is a page with a general degree of importance, 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, X is a total page browsing amount corresponding to the target page browsed in the target session, Y is a browsing amount of an important page in the total page browsing amount, and X-Y is a browsing amount of a general page in the total page flow.
The calculation module 303 includes:
a second calculation unit 3031 configured to calculate the withdrawal rate GA by:
GA＝M/N*100％；
and M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and N is the sum of exit 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 configured to divide each page in the website into a general page and an important page according to the importance degree of the web page content.
Optionally, in this embodiment of the present invention, the obtaining module 301 may include:
an obtaining unit 3011, configured to obtain a preset number of target sessions from a history session record corresponding to the website.
In this embodiment of the present invention, the obtaining module 301 obtains a preset number of target sessions including target pages, the determining module 302 determines the possibility of exiting the target pages, i.e., exiting weight, according to the browsing sequence of the target pages in the target sessions and the importance degree of the target pages, and the calculating module 303 calculates the exit rate of the target pages according to the exiting weight. According to the method and the device, the page quitting rate can be calculated by combining the importance degree of the page and the access depth of the page in the session, so that the quitting behavior of the user in the page can be evaluated more accurately, and more effective data can be provided for webpage construction.
Secondly, the embodiment of the present invention provides a specific way for determining the exit weight by the determining module 302, which improves the realizability of the scheme.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for calculating web page data, comprising:
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.
2. The method of claim 1, wherein the determining, by the server, 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 level comprises:
for each target session, the server calculates the exit weight of each target page in the target session by the following method:
(1-a)X-Y(1-b)Y；
the method comprises the steps that a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, the general page is a page with a general importance degree, 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, X is a total page browsing amount corresponding to the target page browsed in the target session, Y is a browsing amount of important pages in the total page browsing amount, and X-Y is a browsing amount of the general page in the total page flow.
3. The method according to claim 1 or 2, wherein the server calculating the exit rate of the target page according to the exit weight comprises:
the server calculates the withdrawal rate GA as follows:
GA＝M/N*100％；
the M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and the N is the sum of exit weights corresponding to each target page in the target session.
4. The method according to claim 1 or 2, wherein the server before acquiring a preset number of target sessions comprises:
and the server divides each page in the website into a general page and an important page according to the importance degree of the webpage content.
5. The method of claim 1 or 2, wherein the server obtaining a preset number of target sessions comprises:
and the server acquires a preset number of target sessions from the historical session records corresponding to the website.
6. A server, comprising:
the acquisition module is used for acquiring a preset number of target sessions corresponding to the website, wherein each target session comprises one or more target pages;
the determining module is used for determining 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 calculation module is used for calculating 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 used for calculating the exit weight of each target page in each target session in the following way:
(1-a)X-Y(1-b)Y；
the method comprises the steps that a is a first exit coefficient, the first exit coefficient is an exit probability coefficient corresponding to a preset general page, the general page is a page with a general importance degree, 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, X is a total page browsing amount corresponding to the target page browsed in the target session, Y is a browsing amount of important pages in the total page browsing amount, and X-Y is a browsing amount of the general page in the total page flow.
8. The server according to claim 6 or 7, wherein the computing module comprises:
a second calculation unit configured to calculate the withdrawal rate GA by:
GA＝M/N*100％；
the M is the sum of exit weights corresponding to target pages serving as exit pages in the target session, and the N is the sum of exit weights corresponding to each target page in the target session.
9. The server according to claim 6 or 7, wherein the server further comprises:
and the dividing module is used for dividing each page in the website into a general page and an important page according to the importance degree of the webpage content.
10. The server according to claim 6 or 7, wherein the obtaining module comprises:
and the acquisition unit is used for acquiring preset number of target sessions from the historical session records corresponding to the websites.
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| CN201611042516.8A CN108093013B (en) | 2016-11-23 | 2016-11-23 | Webpage data calculation method and server |
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