CN114154064A - Commodity keyword optimization method and device - Google Patents
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Abstract
The invention discloses a commodity keyword optimization method and a commodity keyword optimization device, wherein the method comprises the following steps: determining at least one alternative keyword and a target commodity; searching each associated commodity corresponding to each alternative keyword; under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords; and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity. In the process, in the process of selecting the target keyword matched with the target commodity, the keyword with the largest flow can be selected from the various alternative keywords capable of searching the target commodity as the target keyword, and because the flow corresponding to the target keyword is the largest, when the target commodity is searched based on the target keyword, the target commodity appears at the position which is ahead in the search result, so that the accuracy of the search result is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a commodity keyword optimization method and device.
Background
In the e-commerce platform, in order to search for a desired commodity more easily, a user usually searches for the desired commodity by inputting a search keyword, and in order to enable the user to find the desired commodity, one or more commodity keywords describing the commodity need to be set for each commodity.
The existing commodity keyword determining process is mainly set based on the attribute of a commodity, and sometimes the same commodity searching keyword is not matched with the commodity keyword, so that a user cannot search for a desired commodity based on the searching keyword, and the searching result is inaccurate.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for optimizing a commodity keyword, so as to solve the problem that a search result is inaccurate because a determination process of an existing commodity keyword is mainly set based on an attribute of a commodity, and sometimes a search keyword for the same commodity is not matched with the commodity keyword, and a user cannot search for a desired commodity based on the search keyword. The specific scheme is as follows:
a commodity keyword optimization method comprises the following steps:
determining at least one alternative keyword and a target commodity;
searching each associated commodity corresponding to each alternative keyword;
under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords;
and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity.
Optionally, the method for searching each associated product corresponding to the candidate keyword includes:
simulating a retrieval request of the alternative keywords based on a keyword search path of the e-commerce platform;
acquiring page original data corresponding to the retrieval request;
and extracting the incidence relation between the alternative keywords and each associated product in the page original data.
Optionally, the method for calculating a total flow of the target product relative to the candidate keywords includes:
determining the target single-day search amount of the target commodity based on the alternative keywords;
and determining the total flow of the target commodity based on the alternative keywords based on the target single-day search volume.
Optionally, the method for determining the single-day search traffic of the target product based on the candidate keyword includes:
acquiring the number of commodities displayed on each page of the E-commerce platform and the total number of the commodities of each associated commodity;
determining the total number of pages occupied by each associated commodity based on the total number of commodities and the number of commodities;
obtaining the loss rate and the current day search quantity of the alternative keywords;
calculating the target single-day search quantity by adopting a first preset calculation formula based on the total page number, the attrition rate and the current-day search quantity of the alternative keywords, wherein the first preset calculation formula is as follows:
wherein, ykRepresents the target single-day search quantity, miThe total number of commodities is shown, m' represents the number of commodities, L represents the loss rate, and f represents the daily search amount of the alternative keywords.
Optionally, the method for determining the total flow of the target product based on the candidate keywords based on the target single-day search volume includes:
acquiring the exposure rate and the click rate of the target commodity;
calculating the total flow of the target commodity based on the alternative keywords by adopting a second preset calculation formula based on the exposure rate, the click rate and the target single-day search volume, wherein the second preset calculation formula is as follows:
wherein Y represents total flow, C represents click rate, b represents a constant,
the exposure is expressed, and n represents the index of the value range.
A commodity keyword optimization apparatus comprising:
the system comprises a determining module, a judging module and a display module, wherein the determining module is used for determining at least one alternative keyword and a target commodity;
the searching module is used for searching each associated commodity corresponding to each alternative keyword;
a calculating module, configured to calculate a total flow of the target product relative to the candidate keywords when the target product exists in the associated products;
and the selecting module is used for selecting the keyword corresponding to the maximum flow from all the flows as the target keyword of the target commodity.
The above apparatus, optionally, the searching module includes:
the simulation unit is used for simulating the retrieval request of the alternative keywords based on the keyword search path of the E-commerce platform;
the acquisition unit is used for acquiring page original data corresponding to the retrieval request;
and the extraction unit is used for extracting the association relationship between the alternative keywords and each associated product in the page original data.
The above apparatus, optionally, the calculating module includes:
a first determining unit, configured to determine a target single-day search amount of the target product based on the candidate keyword;
and the second determining unit is used for determining the total flow of the target commodity based on the alternative keywords based on the target single-day search volume.
The above apparatus, optionally, the first determining unit includes:
the first acquisition subunit is used for acquiring the number of commodities displayed on each page of the e-commerce platform and the total number of the commodities of each associated commodity;
a determining subunit, configured to determine, based on the total number of commodities and the number of commodities, a total number of pages occupied by each associated commodity;
the second obtaining subunit is used for obtaining the attrition rate and the current day search quantity of the alternative keywords;
a first calculating subunit, configured to calculate a target single-day search volume by using a first preset calculation formula based on the total number of pages, the attrition rate, and the current-day search volume of the candidate keywords, where the first preset calculation formula is:
wherein, ykRepresents the target single-day search quantity, miThe total number of commodities is shown, m' represents the number of commodities, L represents the loss rate, and f represents the daily search amount of the alternative keywords.
The above apparatus, optionally, the second determining unit includes:
the third acquisition subunit is used for acquiring the exposure rate and the click rate of the target commodity;
a second calculating subunit, configured to calculate, based on the exposure rate, the click rate, and the target single-day search volume, a total flow of the target product based on the candidate keywords by using a second preset calculation formula, where the second preset calculation formula is:
wherein Y represents total flow, C represents click rate, b represents a constant,
the exposure is expressed, and n represents the index of the value range.
Compared with the prior art, the invention has the following advantages:
the invention discloses a commodity keyword optimization method and a commodity keyword optimization device, wherein the method comprises the following steps: determining at least one alternative keyword and a target commodity; searching each associated commodity corresponding to each alternative keyword; under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords; and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity. In the process, in the process of selecting the target keyword matched with the target commodity, the keyword with the largest flow can be selected from the various alternative keywords capable of searching the target commodity as the target keyword, and because the flow corresponding to the target keyword is the largest, when the target commodity is searched based on the target keyword, the target commodity appears at the position which is ahead in the search result, so that the accuracy of the search result is improved.
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 flowchart of a commodity keyword optimization method disclosed in an embodiment of the present application;
fig. 2 is a flow loss simulation diagram disclosed in an embodiment of the present application;
fig. 3 is a block diagram of a structure of a commodity keyword optimization apparatus according to an embodiment of the present disclosure.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
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.
The invention discloses a commodity keyword optimization method and a device, which are applied to the determination process of commodity keywords in an E-commerce platform, in the prior art, because the commodity keywords are mainly set based on the attributes of commodities, the search keywords and the commodity keywords are not matched aiming at the same commodity sometimes, and a user cannot search the wanted commodity based on the search keywords, so that the search result is not accurate, in order to solve the problems, the invention provides a commodity keyword optimization method, the execution flow of the method is shown in figure 1, and the method comprises the following steps:
s101, determining at least one alternative keyword and a target commodity;
in the embodiment of the invention, the target commodity is a commodity needing keyword matching, the alternative keywords are selected from a keyword library, the keywords are common keywords selected based on experience or specific conditions, and the alternative keywords are selected in the following process that each keyword with the occurrence frequency meeting the preset frequency or ranking as a preset noun in the process of searching the target commodity can be screened out as the alternative keywords according to information disclosed by a seller in an e-commerce platform or background data information of the e-commerce platform, wherein the preset frequency or the preset ranking can be set based on experience or specific conditions, and the embodiment of the invention is not specifically limited.
S102, searching each associated commodity corresponding to each alternative keyword;
in the embodiment of the invention, aiming at each alternative keyword, a keyword search path of the E-commerce platform is spliced through a server data acquisition technology, a search request is simulated, original data of the E-commerce platform for searching the alternative keyword is obtained based on the search request, the original data is cleaned, and commodity association types of different areas in a page captured by the cleaned original data, the alternative keyword and each associated commodity are captured based on the cleaned original data. For example, one binding between an alternative keyword and an associated good may be json data:
keyword is the alternative Keyword
And the asin in the Products is a commodity identification.
And formatting the data into standard structured data, product identifications, keywords, ranking names and the like through a big data platform.
S103, under the condition that the target commodity exists in each related commodity, calculating the total flow of the target commodity relative to the alternative keywords;
in the embodiment of the invention, each associated commodity is traversed, whether the target commodity exists in each associated commodity is judged, and the current alternative keyword is abandoned and the next alternative keyword is continuously selected for retrieval under the condition that the target commodity does not exist in each associated commodity.
In a case where the target product exists in the associated products, a total flow rate of the target product with respect to the candidate keywords is calculated, before calculating the total flow rate, first, a parameter used for calculating the total flow rate needs to be described, and the candidate keywords are input on the e-commerce platform, and then, the associated products corresponding to all the candidate keywords are skipped, preferably, the associated products are a list, and each product has its own position, such as: assuming that we are 48 items per page, the first item rank of the first page is the first name, the first item in the second page is 49 items, the second item is the 50 th name, and so on, the calculation formula is: (p-1) the number of commodities per page is analyzed in sequence, and the ranking is +1 in sequence, wherein p is pages from 1 to n, each associated commodity appears on the corresponding search page, the flow of each page is different, and the flow is reduced along with the increase of the number of pages.
As the number of search pages decreases exponentially, the flow of a single page decreases exponentially as the number of search pages increases.
The number of search pages is set to P (total page number), and the flow of the first page is set to Y1And the loss rate of each page is L, the flow calculation formula obtained by the commodity is as follows:
Yi＝Y1*[(1-L)^Pi] (1)
in the embodiment of the invention, the flow loss rate is set to be 10%.
On the premise that the alternative keywords accord with the characteristics of the commodity, mathematical simulation can be performed on flow loss, a matlab simulation graph is used based on a formula (1) and is shown in fig. 2, the abscissa is the number of searched pages, and the ordinate is single-page flow. As can be seen from fig. 2, the single page traffic exponentially decreases as the number of search pages increases. Therefore, when the page number is larger, the flow loss speed is very high, so that when the keywords are set, the keywords are not all set to be broad words, and a certain proportion of accurate words or high-resolution words is needed, so that the number of pages is reduced as much as possible.
Furthermore, from the flow angle, keywords are optimized, and the search exposure rate and the flow growth rate of products can be increased. Assuming that the total flow rate that a certain commodity can obtain is set as Y, Y is equal to the exposure X of the commodity multiplied by the click rate C, that is, Y ═ X × C, and since the click rate is related to the main product graph, C can be regarded as a constant, wherein the exposure X is equal to the total search quantity F of the keyword multiplied by the search exposure p (X), that is, X ═ F × p (X), and wherein the search exposure p (X) is in inverse proportion to the total quantity M of the related commodities displayed after the keyword search, that is, p (X) +, M-1Or can be marked as
Where b is constant, i.e., the more things that are searched out, the less the exposure.
Further, aiming at the search quantity F, the times that all consumers input the keyword in the e-commerce platform for retrieval are calculated, and aiming at the exposure rate P (x): not all goods are exposed, have certain exposure rate, to exposure X: exposure is the amount of search F exposure p (x), for click rate C: even if the exposure is in front of consumers, not all the commodity consumers click, and the click rate is a certain value, aiming at the flow rate Y: the consumer clicks the flow for browsing the commodity detail page, and aiming at the flow loss rate L: on the related commodities searched by the alternative keywords, the more backward pages are, the larger the flow loss is.
Further, the above derivation is total data, not single day data, defining single day traffic as yi(i is more than or equal to 1 and less than or equal to n) and the single-day exposure is xi(i is more than or equal to 1 and less than or equal to n) and the search quantity of the keywords in a single day is fi(i is more than or equal to 1 and less than or equal to n), and the total number of commodities searched in a single day is mi(1. ltoreq. i.ltoreq.n), so the above formula can be arranged as the following equation:
wherein Y represents total flow, C represents click rate, b represents a constant,
representing exposure, n representing a mark of a value range, fiRepresenting the daily search volume of the alternative keywords.
From the above formula, it can be seen that the search exposure rate P (X) is inversely proportional to the number M of products displayed after the keyword search, i.e., P (X) · M-1. However, this inverse relationship is relatively less stringent because, even though the pages are smaller, the flow of items on the pages behind must be less than the flow on the pages in front.
Therefore, in the embodiment of the present invention, the traffic loss rate L is introduced, which indicates that the traffic is less due to the increase of the number of pages.
The target single-day search amount of the k-th page is
Wherein, ykRepresents the target single-day search quantity, miRepresenting the total number of commodities, m' representing the number of commodities, L representing the loss rate, fiRepresenting the daily search volume of the alternative keywords.
Equation (2) is converted into the following form based on equation (3):
and calculating the total flow corresponding to each alternative keyword based on the method.
And S104, selecting the keyword corresponding to the maximum flow from all the flows as the target keyword of the target commodity.
In the embodiment of the invention, aiming at the total flow of all the alternative keywords corresponding to the target commodity, the alternative keyword corresponding to the maximum flow is selected from the corresponding total flow to serve as the target keyword of the target commodity.
The invention discloses a commodity keyword optimization method, which comprises the following steps: determining at least one alternative keyword and a target commodity; searching each associated commodity corresponding to each alternative keyword; under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords; and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity. In the process, in the process of selecting the target keyword matched with the target commodity, the keyword with the largest flow can be selected from the various alternative keywords capable of searching the target commodity as the target keyword, and because the flow corresponding to the target keyword is the largest, when the target commodity is searched based on the target keyword, the target commodity appears at the position which is ahead in the search result, so that the accuracy of the search result is improved.
In the embodiment of the invention, the flow index of the user recommended flow words can be calculated by combining the keyword acquisition data, the captured search page number and the ranking. For example, when the first page flow rate is 10000 and the second page flow rate is 4000, the loss rate L of the flow rate is 0.6 and the retention rate of the flow rate is 0.4.
If the daily search amount of a certain keyword is 200000 times and the total number of commodities searched by the keyword in a single day is 500, m isiThis time is 500, assuming that the product is just on shelf and the exposure page of the product row in the 500 products is the last page, if the number of the products displayed on a single page on the e-commerce platform is calculated according to 48, that is to say, the product appears on the 11 th page, assuming that the loss rate of each page is the same and is 60%, then this time, the product is displayed on the 11 th page
Can deduce
That is, 0.4^11 × 200000 ≈ 8.4 search volumes, and then the two factors of exposure rate and click rate are added to calculate the flow of the commodity on page 11.
In the embodiment of the invention, the keyword library on the e-commerce platform is used for inquiring the associated commodities of each keyword, if the to-be-detected commodities appear in the associated commodities, the keywords are alternative keywords, the flow of the to-be-detected commodities in the alternative keywords is calculated by combining the search amount of the keywords, the number of the associated commodities appearing after keyword search, the page of the to-be-detected commodities in the associated commodities, the exposure rate, the flow loss rate and the like, and the flow relation between each alternative keyword and the commodities is reversely analyzed, so that the target keywords are selected.
Based on the above method for optimizing commodity keywords, in the embodiment of the present invention, a commodity keyword optimization device is further determined, and a structural block diagram of the device is shown in fig. 3, where the device includes:
a determination module 201, a search module 202, a calculation module 203 and a selection module 204.
Wherein the content of the first and second substances,
the determining module 201 is configured to determine at least one alternative keyword and a target product;
the search module 202 is configured to search, for each alternative keyword, each associated product corresponding to the alternative keyword;
the calculating module 203 is configured to calculate a total flow of the target product relative to the candidate keywords when the target product exists in the associated products;
the selecting module 204 is configured to select a keyword corresponding to the maximum flow rate from the flow rates as a target keyword of the target product.
The invention discloses a commodity keyword optimization device, which comprises: determining at least one alternative keyword and a target commodity; searching each associated commodity corresponding to each alternative keyword; under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords; and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity. In the process, in the process of selecting the target keyword matched with the target commodity, the keyword with the largest flow can be selected from the various alternative keywords capable of searching the target commodity as the target keyword, and because the flow corresponding to the target keyword is the largest, when the target commodity is searched based on the target keyword, the target commodity appears at the position which is ahead in the search result, so that the accuracy of the search result is improved.
In this embodiment of the present invention, the searching module 202 includes:
a simulation unit 205, an acquisition unit 206, and an extraction unit 207.
Wherein the content of the first and second substances,
the simulation unit 205 is configured to simulate a retrieval request of the alternative keyword based on a keyword search path of an e-commerce platform;
the obtaining unit 206 is configured to obtain page original data corresponding to the retrieval request;
the extracting unit 207 is configured to extract an association relationship between the candidate keyword and each associated product in the page original data.
In this embodiment of the present invention, the calculating module 203 includes:
a first determining unit 208 and a second determining unit 209.
Wherein the content of the first and second substances,
the first determining unit 208 is configured to determine a target single-day search amount of the target product based on the candidate keyword;
the second determining unit 209 is configured to determine, based on the target single-day search volume, a total flow of the target product based on the candidate keywords.
In this embodiment of the present invention, the first determining unit 208 includes:
a first acquisition subunit 210, a determination subunit 211, a second acquisition subunit 212, and a first calculation subunit 213.
Wherein the content of the first and second substances,
the first obtaining subunit 210 is configured to obtain the number of commodities displayed on each page of the e-commerce platform and the total number of commodities of each associated commodity;
the determining subunit 211, configured to determine, based on the total number of commodities and the number of commodities, a total number of pages occupied by each associated commodity;
the second obtaining subunit 212 is configured to obtain a churn rate and a current daily search amount of the alternative keyword;
the first calculating subunit 213 is configured to calculate a target single-day search amount by using a first preset calculation formula based on the total number of pages, the attrition rate, and the current-day search amount of the candidate keyword, where the first preset calculation formula is:
wherein, ykRepresents the target single-day search quantity, miThe total number of commodities is shown, m' represents the number of commodities, L represents the loss rate, and f represents the daily search amount of the alternative keywords.
In this embodiment of the present invention, the second determining unit 209 includes:
a third acquisition subunit 214 and a second calculation subunit 215.
Wherein the content of the first and second substances,
the third obtaining subunit 214 is configured to obtain an exposure rate and a click rate of the target product;
the second calculating subunit 215 is configured to calculate, based on the exposure rate, the click rate, and the target single-day search volume, a total flow of the target product based on the candidate keyword by using a second preset calculation formula, where the second preset calculation formula is:
wherein Y representsTotal flow, C click rate, b constant,
the exposure is expressed, and n represents the index of the value range.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A commodity keyword optimization method is characterized by comprising the following steps:
determining at least one alternative keyword and a target commodity;
searching each associated commodity corresponding to each alternative keyword;
under the condition that the target commodity exists in the related commodities, calculating the total flow of the target commodity relative to the alternative keywords;
and selecting a keyword corresponding to the maximum flow from all the flows as a target keyword of the target commodity.
2. The method of claim 1, wherein finding each associated item corresponding to the candidate keyword comprises:
simulating a retrieval request of the alternative keywords based on a keyword search path of the e-commerce platform;
acquiring page original data corresponding to the retrieval request;
and extracting the incidence relation between the alternative keywords and each associated product in the page original data.
3. The method of claim 1, wherein calculating a total flow of the target commodity relative to the candidate keywords comprises:
determining the target single-day search amount of the target commodity based on the alternative keywords;
and determining the total flow of the target commodity based on the alternative keywords based on the target single-day search volume.
4. The method of claim 3, wherein determining the single-day search traffic for the target commodity based on the candidate keywords comprises:
acquiring the number of commodities displayed on each page of the E-commerce platform and the total number of the commodities of each associated commodity;
determining the total number of pages occupied by each associated commodity based on the total number of commodities and the number of commodities;
obtaining the loss rate and the current day search quantity of the alternative keywords;
calculating the target single-day search quantity by adopting a first preset calculation formula based on the total page number, the attrition rate and the current-day search quantity of the alternative keywords, wherein the first preset calculation formula is as follows:
wherein, ykRepresents the target single-day search quantity, miThe total number of commodities is shown, m' represents the number of commodities, L represents the loss rate, and f represents the daily search amount of the alternative keywords.
5. The method of claim 3, wherein determining the total flow of the target product based on the candidate keywords based on the target single-day search volume comprises:
acquiring the exposure rate and the click rate of the target commodity;
calculating the total flow of the target commodity based on the alternative keywords by adopting a second preset calculation formula based on the exposure rate, the click rate and the target single-day search volume, wherein the second preset calculation formula is as follows:
wherein Y represents total flow, C represents click rate, b represents a constant,
the exposure is expressed, and n represents the index of the value range.
6. A commodity keyword optimization device, comprising:
the system comprises a determining module, a judging module and a display module, wherein the determining module is used for determining at least one alternative keyword and a target commodity;
the searching module is used for searching each associated commodity corresponding to each alternative keyword;
a calculating module, configured to calculate a total flow of the target product relative to the candidate keywords when the target product exists in the associated products;
and the selecting module is used for selecting the keyword corresponding to the maximum flow from all the flows as the target keyword of the target commodity.
7. The apparatus of claim 6, wherein the lookup module comprises:
the simulation unit is used for simulating the retrieval request of the alternative keywords based on the keyword search path of the E-commerce platform;
the acquisition unit is used for acquiring page original data corresponding to the retrieval request;
and the extraction unit is used for extracting the association relationship between the alternative keywords and each associated product in the page original data.
8. The apparatus of claim 6, wherein the computing module comprises:
a first determining unit, configured to determine a target single-day search amount of the target product based on the candidate keyword;
and the second determining unit is used for determining the total flow of the target commodity based on the alternative keywords based on the target single-day search volume.
9. The apparatus according to claim 8, wherein the first determining unit comprises:
the first acquisition subunit is used for acquiring the number of commodities displayed on each page of the e-commerce platform and the total number of the commodities of each associated commodity;
a determining subunit, configured to determine, based on the total number of commodities and the number of commodities, a total number of pages occupied by each associated commodity;
the second obtaining subunit is used for obtaining the attrition rate and the current day search quantity of the alternative keywords;
a first calculating subunit, configured to calculate a target single-day search volume by using a first preset calculation formula based on the total number of pages, the attrition rate, and the current-day search volume of the candidate keywords, where the first preset calculation formula is:
wherein, ykRepresents the target single-day search quantity, miThe total number of commodities is shown, m' represents the number of commodities, L represents the loss rate, and f represents the daily search amount of the alternative keywords.
10. The apparatus according to claim 8, wherein the second determining unit comprises:
the third acquisition subunit is used for acquiring the exposure rate and the click rate of the target commodity;
a second calculating subunit, configured to calculate, based on the exposure rate, the click rate, and the target single-day search volume, a total flow of the target product based on the candidate keywords by using a second preset calculation formula, where the second preset calculation formula is:
wherein Y represents total flow, C represents click rate, b represents a constant,
the exposure is expressed, and n represents the index of the value range.
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