CN112036988B - Label generation method and device, storage medium and electronic equipment - Google Patents
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Abstract
The invention discloses a label generation method and device, a storage medium and electronic equipment. Wherein, the method comprises the following steps: the method comprises the steps of obtaining a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop; responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with the reference shop, wherein the reference shop and the target shop establish a relationship; acquiring a commodity with a recommended label from a target shop as a target commodity; and displaying a target label of the target commodity in the first recommendation area. The invention solves the technical problem that the content of the display page is single due to the lack of the recommended content.
Description
Technical Field
The invention relates to the field of computers, in particular to a label generation method and device, a storage medium and electronic equipment.
Background
With the development of online shopping, in order to display the products that may be interested by the user in the currently displayed page, the shopping platform generally detects the user behavior, forms tags for the products that may be interested by the user, and displays the tags on the currently browsed page of the user.
In the prior art, the selection of the commodity of interest is usually based on the detection and statistics of user behavior and browsing of the platform commodity. However, in the e-commerce platform of SAAS (software as a service) mode, a user can only see goods of one shop in the platform, and thus, the problem that the user behavior is too little to form interested related recommendations, and a recommendation bar is vacant, and the displayed page content is relatively single may occur.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a tag generation method and device, a storage medium and electronic equipment, which are used for at least solving the technical problem that the content of a display page is single due to the lack of recommended content.
According to an aspect of an embodiment of the present invention, there is provided a tag generation method, including: acquiring a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop; responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with a reference shop, wherein the reference shop and the target shop establish a relationship; acquiring the goods with the recommended labels from the target shops as the target goods; and displaying the target label of the target commodity in the first recommendation area.
According to another aspect of the embodiments of the present invention, there is also provided a tag generation apparatus, including: the system comprises a first obtaining module, a second obtaining module and a display module, wherein the first obtaining module is used for obtaining a display request for requesting to display a first recommendation area matched with a target shop, and the first recommendation area is used for displaying a target label of a target commodity in the target shop; a second obtaining module, configured to respond to the display request, to obtain a recommended label displayed in a second recommended area matched with a reference store, where the reference store and the target store have a relationship; a third obtaining module, configured to obtain, from the target store, a product with the recommended label as the target product; and a display module, configured to display the target tag of the target product in the first recommended area.
According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above tag generation method when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory and a processor, where the memory stores therein a computer program, and the processor is configured to execute the above tag generation method through the computer program.
In the embodiment of the invention, by acquiring the recommended label displayed by the reference shop associated with the target shop in the second recommended area, and acquiring the target label corresponding to the target commodity corresponding to the recommended label in the target shop and displaying the target label in the first recommended area, the purpose of determining the target label for recommended display in the target shop is achieved, so that the technical effect of displaying the target label of the target shop in the recommended area is realized, and the technical problem that the displayed page content is single due to the lack of the recommended content is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic diagram of an application environment of an alternative tag generation method according to an embodiment of the invention;
FIG. 2 is a flow diagram illustrating an alternative label generation method according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of a display page of an alternative tag generation method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart diagram illustrating yet another alternative label generation method according to an embodiment of the present invention;
FIG. 5 is a flow chart diagram illustrating yet another alternative label generation method according to an embodiment of the present invention;
FIG. 6 is a schematic flow chart diagram illustrating yet another alternative label generation method according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart diagram illustrating yet another alternative label generation method according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of an alternative label generation apparatus according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an alternative electronic device according to an embodiment of the invention;
fig. 10 is a schematic structural diagram of another alternative electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above 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 capable of operation in other sequences than those illustrated or 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.
According to an aspect of the embodiments of the present invention, there is provided a label generation method, optionally, the label generation method may be applied, but not limited to, in an environment as shown in fig. 1. Terminal device 102 interacts with server 112 through network 110.
Alternatively, server 112 may, but is not limited to, running the following steps:
the method comprises the steps of S1, obtaining a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop;
s2, responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with the reference shop, wherein the reference shop and the target shop establish a relationship;
and S3, acquiring the commodity with the recommended label from the target shop as the target commodity.
The server 112 determines the target product by running the above steps, and generates a corresponding target tag, so that the terminal device 102 displays the target tag in the first recommended area of the display page of the terminal device, so as to achieve an effect of enriching the current display page.
Optionally, in this embodiment of the present application, the terminal device 102 may be a terminal device configured with a target client, and may include but is not limited to at least one of the following: mobile phones (such as Android phones, iOS phones, etc.), notebook computers, tablet computers, palm computers, MID (Mobile Internet Devices), PAD, desktop computers, smart televisions, etc. The target client may be a client with web shopping capabilities. The form of the online shopping function can be but is not limited to: web pages, links, applets, owned malls, etc. The network 110 may include, but is not limited to: a wired network, a wireless network, wherein the wired network comprises: a local area network, a metropolitan area network, and a wide area network, the wireless network comprising: bluetooth, WIFI, and other networks that enable wireless communication. The server 112 may be a single server, a server cluster composed of a plurality of servers, or a cloud server. The above is only an example, and this is not limited in this embodiment.
As an optional implementation manner, as shown in fig. 2, the tag generation method includes:
s202, a display request for requesting to display a first recommendation area matched with a target shop is obtained, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop;
s204, responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with the reference shop, wherein the reference shop and the target shop establish a relationship;
s206, acquiring the goods with the recommended labels from the target shops as target goods;
and S208, displaying the target label of the target commodity in the first recommendation area.
Alternatively, the target store may be an electronic store that is unable to generate a recommendation bar tag based on its shopping behavior. Alternatively, the reason why the recommendation bar tag cannot be generated based on self-shopping behavior may be, but is not limited to: the shops contain less user behaviors and the current user purchases less.
Optionally, the first recommendation area may be a tag display area for displaying a tag corresponding to an article included in the target store. Alternatively, the first recommendation area may be, but is not limited to: a recommendation area in a recommendation page, a recommendation area in a shopping cart page, a recommendation area in an order page, and a recommendation area in a current page. Optionally, the first recommendation region is a region corresponding to a second recommendation region in the reference shop.
Optionally, the target tag may be a display tag corresponding to a corresponding product in the target store, and the display page of the target product may be skipped through the target tag by a click operation or other operation modes, so as to browse the detailed information of the target product.
Alternatively, the reference store may be an electronic store that may filter out the recommended tags based on the user behavior detected by itself. The reference shops contain more user behaviors, so that the commodities which are possibly interested by the user can be selected from the commodities contained in the reference shops based on more user behaviors, and corresponding recommendation labels are formed and displayed in corresponding recommendation areas.
Alternatively, the method of forming the recommendation label in the second recommendation area with reference to the shop may be, but is not limited to: collaborative filtering, network-based recommendation algorithms, and association analysis.
Optionally, the association relationship between the reference shop and the target shop may be a correspondence relationship bound based on the similarity of the goods contained in the shop. Optionally, one target store has an association relationship with only one reference store, and one reference store has an association relationship with a plurality of target stores. Alternatively, the similarity of the goods may be, but is not limited to: uniform in category (e.g., all garments), uniform in specific category (e.g., all men's garments, all shirts).
Optionally, as shown in fig. 3, the currently displayed page is taken as a recommended page in a shop as an example. In the current recommendation page, a shop name, a search bar, a plurality of activity tags, and a recommendation area for displaying a goods tag for recommendation are displayed. It can be known that, a plurality of display spaces for displaying the target tags 304 are currently set in the first recommendation region 302 of the target store, and currently, because the user behaviors in the target store are relatively few, the target tags 304 cannot be generated by screening the target products and displayed in the first recommendation region 302. The method comprises the steps of obtaining a request that a target label 304 is displayed in a first recommendation area 302 on a recommendation page of a current target shop, after the display request is obtained, determining a reference shop related to the target shop according to an association relation, obtaining recommendation labels 314 displayed in a second recommendation area 312 in the recommendation page of the reference shop, wherein the number of the recommendation labels 314 is not limited, obtaining commodities with the recommendation labels 314 in a commodity list of the target shop according to the recommendation labels 314 to serve as target commodities, and generating the target label 304 so as to display the target label 304 in the first recommendation area 302.
In the embodiment of the application, by acquiring the recommended label displayed in the second recommended area by the reference shop associated with the target shop, and acquiring the target label corresponding to the target commodity corresponding to the recommended label in the target shop and displaying the target label in the first recommended area, the purpose of determining the target label for recommended display in the target shop is achieved, so that the technical effect of displaying the target label of the target shop in the recommended area is achieved, and the technical problem that the content of the displayed page is single due to the lack of the recommended content is solved.
As an alternative embodiment, as shown in fig. 4, before obtaining the item with the recommended label from the target store as the target item, the method further includes:
s402, performing word segmentation processing on the recommended labels to obtain a plurality of recommended phrases;
s404, acquiring recommended phrase vectors corresponding to the recommended phrases respectively;
s406, acquiring the distribution weight of each recommended phrase in the recommended label;
and S408, calculating a recommendation vector corresponding to the recommendation label according to the recommended phrase vector and the distribution weight.
Optionally, the word segmentation process performed on the recommended labels may be, but is not limited to: and performing word segmentation processing by using a word segmentation algorithm. Alternatively, the word segmentation algorithm may be, but is not limited to: jieba, pynlpir, snornlp.
Optionally, the word segmentation processing based on the recommended tags to obtain the recommended word group may be, but is not limited to: and reserving the phrases existing in the database as recommended phrases. Alternatively, the database may be, but is not limited to: a database of word segmentation algorithm and a preset special database.
Alternatively, the recommended phrase vector for obtaining the recommended phrases may be, but is not limited to: and obtaining a recommended phrase vector by using a word vector model. Alternatively, the word vector model may be, but is not limited to: AI Lab word vector model.
Alternatively, the assigned weight of the recommended phrase in the recommended label may be, but is not limited to: the proportion of the word meaning of the recommended word group and the proportion of the word frequency of the recommended word group.
As an alternative implementation, the obtaining the assigned weight of each of the plurality of recommended phrases in the recommended tags includes:
determining the occurrence frequency of the recommended phrases in a commodity list of a reference shop, and determining the phrase weights of the recommended phrases according to the occurrence frequency of the recommended phrases;
and calculating the distribution weight of the recommended phrases in the recommended labels according to the respective phrase weights of all the recommended phrases in the recommended labels.
Alternatively, the phrase weight of the recommended phrase may be preset. Alternatively, determining the phrase weight of the recommended phrase according to the frequency of occurrence of the recommended phrase in the reference shop may be, but is not limited to: the higher the occurrence frequency of the recommended phrases is, the smaller the corresponding weight of the phrases is. For example, if the reference shop is a men's shop, the phrase "men's clothing" appears frequently in the product tag list of the shop, and thus the corresponding phrase weight is small, and if the phrase "men's clothing" appears frequently in the product tag list, it is assumed that the phrase "stamp" is set to 0.01, and the phrase "stamp" appears frequently in the product tag list, it is assumed that the phrase "stamp" is set to 0.05. The recommended phrase with the characteristic of the commodity is highlighted in the recommended label, and the corresponding characteristic of the recommended label is determined conveniently.
Alternatively, the assigned weight may be, but is not limited to, the proportion of the phrase weight of the recommended phrase to the sum of the phrase weights of all the recommended phrases of the recommended tags.
As an optional implementation manner, calculating a recommendation vector corresponding to the recommendation label according to the recommended phrase vector and the assigned weight includes:
acquiring a second indication vector of the recommended phrase according to the phrase vector and the distribution weight of the recommended phrase;
acquiring second indication vectors of all recommended phrases;
and taking the average value of all the second indication vectors as a recommendation vector.
Alternatively, the second indication vector may be, but is not limited to: the product of the phrase vector of the recommended phrase and the assigned weight.
In the embodiment of the application, the distribution weight of the recommended phrases in the recommended labels is determined according to the determined phrase vectors and the phrase weights of the recommended phrases, the average value of the product of the phrase vectors of all the recommended phrases and the distribution weight is used as the recommended vector, so that the recommended phrases in the recommended labels can be embodied by the distribution weight, the recommended phrases contained in the recommended labels are covered by the average value representing the recommended vectors, the recommended vectors can represent the recommended labels, and the recommended labels are digitalized.
As an alternative embodiment, as shown in fig. 5, the obtaining the goods with the recommended label from the target store as the target goods includes:
s502, obtaining a reference vector corresponding to a commodity label of each commodity in a target shop;
s504, sequentially comparing the reference vector with the recommended vector to obtain the vector distance corresponding to each commodity;
s506, taking the commodity with the vector distance smaller than the first threshold value as a candidate commodity;
and S508, selecting the target commodity from the candidate commodities according to the size of the display area of the first recommendation area.
It should be noted that: the vector distance is a distance value obtained by calculation between the corresponding reference vector and the recommended vector. The first threshold is a preset numerical value, and is used for judging whether the distance of the reference vector meets the condition that the corresponding commodity is taken as a candidate commodity.
Optionally, according to the size of the display area of the first recommendation area, the picking of the target goods from the candidate goods may be determined according to the vector sorting of the candidate goods, or determined according to the classification sorting of the candidate goods. Alternatively, the classification of the candidate item may be a classification according to a difference of the recommendation labels in the corresponding second recommendation regions. Optionally, the number of recommendation tags displayed in the second recommendation area is not limited. Optionally, a recommendation vector of each recommended label is calculated, the reference vector of the commodity label in the target shop is compared with each recommendation vector respectively, and the reference vectors are sorted according to the vector distance from small to large respectively.
Alternatively, the value of the first threshold may be determined according to the size of the display area of the first recommendation area. Alternatively, the value of the first threshold may have values corresponding to different levels. Optionally, the determination of the numerical rating of the first threshold is associated with the size of the display area of the first recommendation area.
Alternatively, when the number of recommended tags matches the number of tags that can be displayed in the first recommended area, the target product is the product located at the first candidate position corresponding to each recommended tag in the candidate products.
Optionally, when the number of the recommended tags is smaller than the number of tags that can be displayed in the first recommended area, the candidate commodities are sorted from small to large according to the vector distance, and the candidate commodities with the number consistent with the number displayed in the first recommended area are taken as the target commodities.
Optionally, under the condition that the number of the recommended labels is smaller than the number of labels that can be displayed in the first recommended area, the candidate commodities are classified according to the corresponding recommended labels, and the candidate commodities with the same number as the number displayed in the first recommended area are sequentially selected as the target commodity in each class of candidate commodities according to the vector distance sorting.
Optionally, when the number of the recommended tags is greater than the number of tags that can be displayed in the first recommended area, the candidate commodities are sorted from small to large according to the vector distance, and the candidate commodities whose number is consistent with the number displayed in the first recommended area are taken as the target commodities.
In the embodiment of the application, candidate commodities are determined according to the reference vector of each commodity in the target shop and the recommendation vector of the recommendation label through comparison, and the target shop is selected according to the vector distance between the reference vector and the recommendation vector, so that the target label in the first recommendation area is displayed. The target labels in the first recommendation areas corresponding to the target shops are generated by referring to the recommendation labels in the shops, so that the problem that the content of the first recommendation areas is vacant due to lack of data of the target shops is solved.
As an optional implementation manner, as shown in fig. 6, the obtaining of the reference vector corresponding to the item tag of each item in the target store includes:
s602, acquiring a commodity label of each commodity in a target shop;
s604, performing word segmentation processing on the commodity label to obtain a plurality of reference phrases;
s606, obtaining a reference phrase vector corresponding to each of the plurality of reference phrases;
s608, acquiring the reference weight of each of the plurality of reference phrases in the commodity label;
and S610, calculating a reference vector corresponding to the commodity label according to the reference phrase vector and the reference weight.
Optionally, the word segmentation process performed on the commodity label may be, but is not limited to: and performing word segmentation processing by using a word segmentation algorithm. Alternatively, the word segmentation algorithm may be, but is not limited to: jieba, pynlpir, snornlp.
Optionally, the reference phrase obtained by the item tag word segmentation process may be, but is not limited to: and reserving phrases existing in the database as recommended phrases. Alternatively, the database may be, but is not limited to: a database of word segmentation algorithm and a preset special database.
Optionally, a reference phrase vector of the reference phrase is obtained to include but not limited to: and obtaining a recommended phrase vector by using a word vector model. Alternatively, the word vector model may be, but is not limited to: AI Lab word vector model.
Alternatively, the reference weight of the reference phrase in the article tag may be, but is not limited to: the proportion of the word meaning of the reference word group and the proportion of the word frequency of the reference word group.
As an alternative implementation, as shown in fig. 7, the obtaining reference weights of a plurality of reference phrases in a product label, and calculating a reference vector corresponding to the product label according to the reference phrase vector and the reference weights includes:
s702, determining the appearance frequency of the reference phrases in the commodity list of the target shop, and determining the phrase weights of the reference phrases according to the appearance frequency of the reference phrases;
s704, calculating reference weights of the reference phrases in the commodity labels according to the phrase weights of all the reference phrases in the commodity labels;
s706, obtaining a first indication vector of the reference phrase according to the phrase vector of the reference phrase and the reference weight;
s708, acquiring first indication vectors of all reference phrases;
s710, using an average value of all the first indication vectors as a reference vector.
Alternatively, the phrase weight of the reference phrase may be preset. Alternatively, determining the phrase weight of the reference phrase according to the frequency of occurrence of the reference phrase in the target store may be, but is not limited to: the higher the appearance frequency of the reference phrase is, the smaller the corresponding weight of the phrase is. For example, the reference shop is a women's dress shop, and the phrase "women's trousers" appears frequently in the product tag list of the reference shop, so the phrase weight corresponding to the phrase "women's trousers" is small, and it is assumed that the phrase weight of the phrase "women's trousers" is set to 0.02. In the item tag list of the reference store, the frequency of appearance of the phrase "V-neck" is low, and therefore the weight of the phrase corresponding to the phrase "V-neck" is large, and it is assumed that the weight of the phrase "V-neck" is set to 0.05. Therefore, the reference phrase with the commodity characteristics is highlighted in the commodity label, and the corresponding characteristics of the commodity label are determined conveniently.
Alternatively, the reference weight may be, but is not limited to, a ratio of the phrase weight of the reference phrase to the sum of the phrase weights of all reference phrases of the item tag.
Alternatively, the first indication vector may be, but is not limited to: the product of the phrase vector of the reference phrase and the reference weight.
In the embodiment of the application, the reference weight of the reference phrase in the commodity label is determined according to the phrase vector and the phrase weight for determining the reference phrase, and the average value of the product of the phrase vector and the reference weight of all the reference phrases is used as the reference vector, so that the characteristic reference phrase in the commodity label can be embodied through the reference weight, and the reference phrase contained in the commodity label is covered through the average value serving as the reference vector, so that the reference vector can represent the commodity label, and the commodity label is digitalized, so that the target label close to the recommended label is selected from the commodity label and displayed in the first recommended area, and the current display page is enriched.
It should be noted that for simplicity of description, the above-mentioned method embodiments are shown as a series of combinations of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
According to another aspect of the embodiment of the invention, a label generation device for implementing the label generation method is also provided. As shown in fig. 8, the apparatus includes:
a first obtaining module 802, configured to obtain a display request for requesting display of a first recommended area matched with a target store, where the first recommended area is used to display a target tag of a target product in the target store;
the second obtaining module 804 is configured to, in response to the display request, obtain a recommendation tag displayed in a second recommendation area matched with the reference shop, where the reference shop and the target shop establish a relationship;
a third obtaining module 806, configured to obtain a product with a recommended label from a target store as a target product;
a display module 808, configured to display a target tag of the target product in the first recommendation area.
In the embodiment of the application, by acquiring the recommended label displayed in the second recommended area by the reference shop associated with the target shop, and acquiring the target label corresponding to the target commodity corresponding to the recommended label in the target shop and displaying the target label in the first recommended area, the purpose of determining the target label for recommended display in the target shop is achieved, so that the technical effect of displaying the target label of the target shop in the recommended area is achieved, and the technical problem that the content of the displayed page is single due to the lack of the recommended content is solved.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device for implementing the above tag generation method, where the electronic device may be the terminal device or the server shown in fig. 1. The present embodiment takes the electronic device as a terminal device as an example for explanation. As shown in fig. 9, the electronic device comprises a memory 902 and a processor 904, the memory 902 having stored therein a computer program, the processor 904 being arranged to perform the steps of any of the above-described method embodiments by means of the computer program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
the method comprises the steps of S1, obtaining a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop;
s2, responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with the reference shop, wherein the reference shop and the target shop establish a relationship;
s3, obtaining a commodity with a recommended label from a target shop as a target commodity;
and S4, displaying a target label of the target commodity in the first recommendation area.
Alternatively, it may be understood by those skilled in the art that the structure shown in fig. 9 is only an illustration, and the electronic device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, and a Mobile Internet Device (MID), a PAD, and the like. Fig. 9 does not limit the structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 9, or have a different configuration than shown in FIG. 9.
The memory 902 may be used to store software programs and modules, such as program instructions/modules corresponding to the tag generation method and apparatus in the embodiments of the present invention, and the processor 904 executes various functional applications and data processing by running the software programs and modules stored in the memory 902, so as to implement the above-described tag generation method. The memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 902 may further include memory located remotely from the processor 904, which may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The storage 902 may be specifically, but not limited to, used for storing information such as sample characteristics of an item and a target virtual resource account number. As an example, as shown in fig. 10, the memory 1002 may include, but is not limited to, a first obtaining module 802, a second obtaining module 804, a third obtaining module 806, and a display module 808 of the label generating apparatus. In addition, the label generating apparatus may further include, but is not limited to, other module units in the label generating apparatus, which are not described in detail in this example.
Optionally, the transmission device 1006 is used for receiving or sending data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 1006 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices so as to communicate with the internet or a local area Network. In one example, the transmission device 1006 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In addition, the electronic device further includes: a display 1008 for displaying the information of the order to be processed; and a connection bus 1010 for connecting the respective module parts in the above-described electronic apparatus.
In other embodiments, the terminal device or the server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication. The nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, and other electronic devices, may become a node in the blockchain system by joining the Peer-To-Peer network.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the label generation method provided in the various alternative implementations of the label generation aspect described above. Wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:
the method comprises the steps of S1, obtaining a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop;
s2, responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with the reference shop, wherein the reference shop and the target shop establish a relationship;
s3, obtaining a commodity with a recommended label from a target shop as a target commodity;
and S4, displaying a target label of the target commodity in the first recommendation area.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be essentially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention.
In the above embodiments of the present invention, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described in detail in a certain embodiment.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be 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, units or modules, and may be in an electrical 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 position, or may be distributed on multiple 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 may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A tag generation method, comprising:
the method comprises the steps of obtaining a display request for requesting to display a first recommendation area matched with a target shop, wherein the first recommendation area is used for displaying a target label of a target commodity in the target shop;
responding to the display request, and acquiring a recommendation label displayed in a second recommendation area matched with a reference shop, wherein the reference shop and the target shop establish a relationship;
obtaining a phrase vector corresponding to each of a plurality of recommended phrases in the recommended labels and a distribution weight of each of the plurality of recommended phrases, wherein the distribution weight is the distribution weight of each of the plurality of recommended phrases in the recommended labels, the distribution weight is related to the occurrence frequency of each of the plurality of recommended phrases in the commodity list of the reference shop, and the higher the occurrence frequency is, the smaller the distribution weight is;
calculating a recommendation vector of the recommendation label according to the phrase vector and the distribution weight;
determining candidate commodities from the commodities of the target shop according to the vector distance between the reference vector corresponding to the commodity label of each commodity in the target shop and the recommended vector, and acquiring the target commodities from the candidate commodities;
displaying the target label of the target commodity in the first recommendation area.
2. The method of claim 1, wherein before obtaining the phrase vector corresponding to each of the plurality of recommended phrases, further comprising:
and performing word segmentation processing on the recommended labels to obtain a plurality of recommended phrases.
3. The method of claim 1, wherein determining candidate products from the respective products of the target store according to the vector distance between the reference vector and the recommendation vector corresponding to the product tag of the respective product of the target store, and obtaining the target product from the candidate products comprises:
acquiring a reference vector corresponding to a commodity label of each commodity in the target shop;
sequentially comparing the reference vector with the recommended vector to obtain the vector distance corresponding to each commodity;
taking the commodity with the vector distance smaller than a first threshold value as a candidate commodity;
and selecting the target commodity from the candidate commodities according to the size of the display area of the first recommendation area.
4. The method of claim 3, wherein the obtaining the reference vector corresponding to the item label of each item in the target store comprises:
acquiring the commodity label of each commodity in the target shop;
performing word segmentation processing on the commodity label to obtain a plurality of reference word groups;
acquiring reference phrase vectors corresponding to the plurality of reference phrases;
acquiring the reference weight of each of the plurality of reference phrases in the commodity label;
and calculating a reference vector corresponding to the commodity label according to the reference phrase vector and the reference weight.
5. The method according to claim 4, wherein the obtaining of the reference weight of each of the plurality of reference phrases in the product label, and the calculating of the reference vector corresponding to the product label according to the reference phrase vector and the reference weight comprises:
determining the appearance frequency of the reference phrases in the commodity list of the target shop, and determining the phrase weights of the reference phrases according to the appearance frequency of the reference phrases;
calculating the reference weight of the reference phrase in the commodity label according to the respective phrase weights of all the reference phrases in the commodity label;
acquiring a first indication vector of the reference phrase according to the phrase vector of the reference phrase and the reference weight;
acquiring the first indication vectors of all the reference phrases;
and taking the average value of all the first indication vectors as the reference vector.
6. The method of claim 1, wherein obtaining the assigned weight for each of the plurality of recommended phrases comprises:
determining the occurrence frequency of each of the plurality of recommended phrases in the commodity list of the reference shop, and determining the phrase weights of the plurality of recommended phrases according to the occurrence frequency of the plurality of recommended phrases;
and calculating the distribution weights of the plurality of recommended phrases in the recommended labels according to the respective phrase weights of the plurality of recommended phrases in the recommended labels.
7. The method of claim 1, wherein the calculating the recommendation vector corresponding to the recommended tag according to the recommended phrase vector and the assigned weight comprises:
acquiring a second indication vector of the recommended phrase according to the phrase vector of the recommended phrase and the distribution weight;
acquiring the second indication vectors of all the recommended phrases;
and taking the average value of all the second indication vectors as the recommendation vector.
8. A label generation apparatus, comprising:
the system comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining a display request for requesting to display a first recommendation area matched with a target shop, and the first recommendation area is used for displaying a target label of a target commodity in the target shop;
the second obtaining module is used for responding to the display request and obtaining a recommendation label displayed in a second recommendation area matched with a reference shop, wherein the reference shop and the target shop establish a relationship;
a third obtaining module, configured to obtain a phrase vector corresponding to each of a plurality of recommended phrases in the recommended label, and a distribution weight of each of the plurality of recommended phrases, where the distribution weight is a distribution weight of each of the plurality of recommended phrases in the recommended label, and the distribution weight is related to an occurrence frequency of each of the plurality of recommended phrases in the commodity list of the reference store, and the distribution weight is smaller when the occurrence frequency is higher; calculating a recommendation vector of the recommendation label according to the phrase vector and the distribution weight; determining candidate commodities from the commodities of the target shop according to the vector distance between the reference vector corresponding to the commodity label of each commodity in the target shop and the recommended vector, and acquiring the target commodities from the candidate commodities;
a display module, configured to display the target tag of the target product in the first recommendation area.
9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
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