Label generation method and device, storage medium, and computing device

Abstract

A label generation method and device, a storage medium and a computing device, the method comprising: obtaining a condition data set, the condition data set comprising at least one search condition, each search condition comprising an attribute type and a value range corresponding to the attribute type; according to the condition data set, searching for a target product from a plurality of alternative products that matches the condition data set; if the target product is found, creating a new label and establishing a mapping relationship between the new label and the condition data set. Through the scheme provided by the present application, the product required by the user can be accurately and efficiently found.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to a tag generation method and apparatus, storage medium, and computing device. Background Technology

[0002] In existing technologies, on e-commerce websites and other platforms with search functions, users typically find the products they need by selecting or setting multiple search criteria. This approach is inefficient and inaccurate; therefore, there is an urgent need for an improved technical solution that can help users find the products they need accurately and efficiently. Summary of the Invention

[0003] The technical problem solved by this invention is to provide an improved technical solution so that users can accurately and efficiently find the products they need.

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a tag generation method, the method comprising: acquiring a condition data set, the condition data set including at least one search condition, each search condition including an attribute type and a value range corresponding to the attribute type; searching for a target product matching the condition data set from multiple candidate products based on the condition data set; if the target product is found, creating a new tag and establishing a mapping relationship between the new tag and the condition data set.

[0005] Optionally, the method further includes setting the access permission of the newly added tag to private access, wherein private access means that only users who set the condition data set are allowed to access it.

[0006] Optionally, the method further includes: publishing the new tag so that all users can access the new tag.

[0007] Optionally, the method further includes: obtaining a product information request from a user terminal; parsing the product information request, and if it contains the new tag, determining a set of conditional data associated with the new tag based on the mapping relationship; performing a search based on the set of conditional data, and feeding back the search result to the user terminal.

[0008] Optionally, the method further includes: if the number of product information requests containing the newly added tag is less than a first preset threshold within a first preset time period after the mapping relationship is established, then the newly added tag is deleted.

[0009] Optionally, the method further includes: obtaining user evaluation data for the newly added tag from the user terminal, the evaluation data being user feedback on search results for the newly added tag; after establishing the mapping relationship, calculating the sharing coefficient of the newly added tag every second preset time interval, the sharing coefficient being calculated based on the evaluation data; and deleting the newly added tag when the sharing coefficient is less than a preset coefficient threshold, or when the number of times the sharing coefficient is less than the preset coefficient threshold reaches a second preset threshold.

[0010] Optionally, the method further includes: adding the new tag to a tag set, the tag set including multiple tags, wherein each tag has a mapping relationship with an associated conditional data set; and performing deduplication processing on the tag set according to the conditional data sets associated with each of the multiple tags to update the tag set.

[0011] Optionally, deduplication of the tag set based on the conditional data sets associated with each of the multiple tags includes: calculating attribute similarity based on the attribute types in the first conditional data set and the attribute types in the second conditional data set, wherein the first conditional data set is the conditional data set associated with the first tag, the second conditional data set is the conditional data set associated with the second tag, the first tag is any tag in the tag set, and the second tag is any tag in the tag set other than the first tag; if the attribute similarity is greater than a third preset threshold, then calculating value similarity based on the value range in the first conditional data set and the value range in the second conditional data set; if the value similarity is greater than a fourth preset threshold, then deleting the first tag or the second tag.

[0012] This invention also provides a label generation device, comprising: a condition acquisition module for acquiring a condition data set, the condition data set including at least one search condition, each search condition including an attribute type and a value range corresponding to the attribute type; a search module for searching for a target product matching the condition data set from multiple candidate products based on the condition data set; and a label creation module for creating a new label if the target product is found, and establishing a mapping relationship between the new label and the condition data set.

[0013] This invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the above-described tag generation method.

[0014] This invention also provides a computing device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the steps of the tag generation method described above when running the computer program.

[0015] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0016] In the embodiment of this invention, a conditional data set is obtained, and a target product matching the conditional data set is searched from multiple candidate products based on the conditional data set. Since the conditional data set includes at least one search condition, and each search condition includes an attribute type and a corresponding value range, the found target product can match each search condition. Furthermore, new tags are created, and a mapping relationship is established between the new tags and the conditional data set. When the same user searches for a target product again, or when other users search for a target product, because of the mapping relationship between the new tags and the conditional data set, the user can access and use the new tags to call the conditional data set to search for the target product without needing to set the conditional data set before searching, which improves the efficiency of the user's search. Furthermore, since the new tags are created based on the premise that a matching target product can be found using the aforementioned conditional data set, compared to the scheme where the user sets the search conditions themselves, using new tags for searching improves the accuracy of the user's search.

[0017] Furthermore, in the solution of this embodiment of the invention, the method further includes: obtaining product information requests from user terminals and parsing the product information requests to determine whether they contain new tags. If, within a first preset time period after establishing the mapping relationship, the number of product information requests containing the new tag is less than a first preset threshold, then the new tag is deleted. This approach allows for monitoring the usage of new tags. If the number of times a user accesses a new tag is low within the first preset time period, it indicates that the new tag has poor usability. Deleting the new tag ensures the usability of tags in the tag set.

[0018] Furthermore, in the embodiment of the present invention, the method further includes: obtaining user evaluation data on the newly added tag from the user terminal; after establishing the mapping relationship, calculating the sharing coefficient of the newly added tag every second preset time interval, wherein the sharing coefficient is calculated based on the evaluation data; and deleting the newly added tag when the sharing coefficient is less than a preset coefficient threshold, or when the number of times the sharing coefficient is less than the preset coefficient threshold reaches a second preset threshold. This approach allows for monitoring the usability of newly added tags and timely removal of tags with poor usability, thereby improving the user experience.

[0019] Furthermore, in the embodiment of the present invention, the method further includes: adding new tags to the tag set, and performing deduplication processing on the tag set according to the conditional data sets associated with each of the multiple tags in the tag set, so as to update the tag set. This approach helps to simplify and optimize the tag set, avoids excessive redundancy in the tag set, and improves the user experience. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating an application scenario of a label generation method according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart illustrating a label generation method according to an embodiment of the present invention;

[0022] Figure 3 This is a partial flowchart of another label generation method in an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of a label generation device according to an embodiment of the present invention. Detailed Implementation

[0024] As described in the background section, there is an urgent need for an improved technical solution that can help users find the products they need accurately and efficiently.

[0025] In existing technologies, users typically search for desired products by selecting or setting multiple search criteria. For example, on e-commerce platforms, users search for their desired monitors by setting specific values ​​or ranges for resolution, interface specifications, and refresh rate; similarly, on stock / fund information platforms, users search for their desired financial products by setting specific values ​​or ranges for multiple indicators. Using such methods results in low search efficiency and accuracy.

[0026] Specifically, if users lack the relevant knowledge about the products they need, they are usually unable to set appropriate search criteria, and therefore cannot accurately find the products they require. In other words, the products found are often not what the user needs. Furthermore, for the same user, if they want to find products with the same characteristics or performance, they need to set search criteria each time, which is inefficient and results in a poor user experience.

[0027] To address the aforementioned technical problems, embodiments of the present invention provide a label generation method and apparatus, a storage medium, and a computing device.

[0028] In the embodiment of this invention, a conditional data set is obtained, and a target product matching the conditional data set is searched from multiple candidate products based on the conditional data set. Since the conditional data set includes at least one search condition, and each search condition includes an attribute type and a corresponding value range, the found target product can match each search condition. Furthermore, new tags are created, and a mapping relationship is established between the new tags and the conditional data set. When the same user searches for a target product again, or when other users search for a target product, because of the mapping relationship between the new tags and the conditional data set, the user can access and use the new tags to call the conditional data set to search for the target product without needing to set the conditional data set before searching, which improves the efficiency of the user's search. Furthermore, since the new tags are created based on the premise that a matching target product can be found using the aforementioned conditional data set, compared to the scheme where the user sets the search conditions themselves, using new tags for searching improves the accuracy of the user's search.

[0029] To make the above-mentioned objectives, features and beneficial effects of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] Reference Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of a tag generation method according to an embodiment of the present invention. The tags can be used to find products needed by users. Tags can intuitively reflect the attributes and characteristics of products. Tag names are usually customizable, for example, they can be defined by the user or by the platform maintaining the tags. For example, when the product is a monitor, the tags could be: basic daily necessities, gaming and entertainment, and design-specific, etc., but are not limited to these. As another example, when the product is a stock, the tags could be: high-priced stock, blue-chip stock, and high-performing stock, etc., but are not limited to these.

[0031] like Figure 1 As shown, computing device 11 can be coupled to user terminal 12 (such as user device 1, user device 2, ..., user device n) to interact with user terminal 12. The computing device 11 can be a server or other existing electronic devices with data receiving and processing capabilities.

[0032] User terminal 12 can be a terminal device used by a user, such as a mobile phone, computer, tablet computer, or other suitable terminal device. More specifically, user terminal 1, user terminal 2, ..., user terminal n can be terminal devices used by the same user or terminal devices used by different users; this embodiment of the invention does not impose any limitations on this. Here, n is a positive integer, and this embodiment of the invention does not impose any limitations on the number of user terminals 12.

[0033] The computing device 11 can obtain a set of conditional data from the user terminal 12. This set of conditional data may include at least one search condition, and each search condition includes an attribute type and a corresponding value range for that attribute type. The attribute type depends on the product type. For example, if the product is a monitor, the attribute type may include one or more of the following: resolution, size, dot pitch, refresh rate, response time, color gamut, color depth, brightness, color temperature, contrast ratio, etc. Similarly, if the product is stocks, the attribute type may include one or more of the following: Relative Strength Index (RSI), Stochastic Oscillator, Directional Movement Index (DPI), and Moving Average Convergence Divergence (MAD), etc.

[0034] Furthermore, the computing device 11 can also be coupled to the database 13 to interact with the database 13. The database 13 can be any existing database with data storage function, and the embodiments of the present invention do not limit the type of database 13.

[0035] Database 13 can be used to store data for multiple candidate products. Specifically, database 13 can store attribute data for each candidate product, and the attribute data for each candidate product can be the values ​​of that candidate product for each attribute type. It should be noted that the attribute data for each candidate product can be fixed or can change over time. More specifically, the attribute data for a candidate product can be the attribute data of that candidate product at multiple historical points in time. For example, if the candidate product is stock A, and its attribute data changes over time, database 13 can store the values ​​of stock A for each attribute type (e.g., various indicators) at different historical points in time.

[0036] It should be noted that database 13 may also store other data related to the alternative products. This embodiment of the invention does not limit the data types stored in database 13.

[0037] Furthermore, the computing device 11 can search the product data stored in the database 13 based on the conditional data set obtained from the user terminal 12. If a target product matching the conditional data set is found, a new tag can be created, and a mapping relationship between the new tag and the conditional data set can be established. Further details regarding the tag generation method in this embodiment will be described below and will not be repeated here.

[0038] Reference Figure 2 , Figure 2 This is a flowchart illustrating a tag generation method according to an embodiment of the present invention. The method can be executed by a computing device, which can be any existing device with data receiving and processing capabilities, such as a computer, server, etc., but is not limited thereto. For example, it could be... Figure 1The computing device 11 shown is an example. Figure 2 The label generation method shown may include the following steps:

[0039] Step S201: Obtain the conditional data set;

[0040] Step S202: Based on the condition data set, find the target product that matches the condition data set from multiple candidate products;

[0041] Step S203: If the target product is found, create a new tag and establish a mapping relationship between the new tag and the conditional data set.

[0042] It is understood that, in specific implementations, the method can be implemented using a software program that runs in a processor integrated within the chip or chip module; or, the method can be implemented using hardware or a combination of hardware and software.

[0043] In the specific implementation of step S201, a condition data set can be obtained from an external source. The condition data set can be used to find the target product required by the user. The condition data set can include at least one search condition, and each search condition includes an attribute type and the value range corresponding to that attribute type.

[0044] In a specific example, it can be seen from Figure 1 The user terminal 12 shown obtains a set of conditional data. The user terminal 12 can select from multiple preset search conditions to obtain the set of conditional data. The user can also select one or more attribute types from multiple preset attribute types and set the corresponding value range for each selected attribute type to obtain the set of conditional data, but is not limited to this.

[0045] For more information on conditional datasets, please refer to the section above. Figure 1 The relevant descriptions will not be repeated here.

[0046] In the specific implementation of step S202, a target product matching the condition data set can be found from multiple candidate products based on the condition data set and the attribute data of multiple candidate products. The attribute data of the candidate products refers to the specific values ​​of each attribute type for the candidate products.

[0047] Specifically, the target product refers to the alternative product that matches the conditional data set. "Matching the conditional data set" means satisfying the requirements of each search condition in the conditional data set; in other words, the target product's attribute type values ​​for each search condition must fall within the range of values ​​specified in that search condition.

[0048] More specifically, for each search condition in the conditional dataset, the system can determine whether each candidate product meets the requirements of that search condition based on the range of values ​​for that search condition and the specific values ​​of the attribute types of multiple candidate products for that search condition. Therefore, by determining whether candidate products meet the requirements of each search condition in the conditional dataset, multiple candidate products can be filtered to find the target product.

[0049] If the target product is found, proceed to step S203. If the target product is not found, a search failure notification can be sent to the user terminal to remind the user to modify the condition data set or reset a new condition data set.

[0050] In the specific implementation of step S203, if a target product is found, a new tag can be created. The name of the new tag is customizable and is typically used to briefly describe the target product, such as its attribute characteristics or performance. In other words, the new tag can concisely and intuitively describe the attribute characteristics of the product reflected in the aforementioned conditional data set. In a specific example, the new tag may be obtained from an external source, but it is not limited to this.

[0051] Furthermore, a mapping relationship can be established between the newly added labels and the conditional data set. Therefore, the newly added labels can be used to find matching conditional data sets; that is, the newly added labels can be used in place of the conditional data set.

[0052] Furthermore, access permissions for newly added tags can be configured to meet user needs in different application scenarios.

[0053] In a specific example, the access permission for a newly added tag can be set to private access. Private access means that only users who set the specified criteria can access and use the new tag. More specifically, the new tag can be displayed only on the terminals of users who set the criteria. With this approach, users only need to set the criteria once, and can directly access the new tag to obtain search results when searching for target products, thus improving search efficiency and user experience.

[0054] In another specific example, new tags can be published so that all users can access them. That is, in addition to users who set the aforementioned conditional data set, other users can also access the new tag. Since the new tag can intuitively describe the attribute characteristics of the product reflected in the conditional data set, other users can directly access the new tag without understanding or setting the specific content of the conditional data set, thus improving the efficiency and accuracy of the search.

[0055] Furthermore, the newly added tags, conditional data sets, and the mapping relationship between them can also be saved.

[0056] In a concrete example, new tags can be added to a tag set. More specifically, new tags, their corresponding conditional data sets, and the relationships between them can be added to the tag set. Thus, the tag set can include multiple tags, each with a mapping relationship to its associated conditional data set. In other words, each tag in the tag set is associated with a conditional data set, and the relationship between each tag and its associated conditional data set is the mapping relationship between that tag and its associated conditional data set. Each tag in the tag set can briefly describe the products that match its associated conditional data set. For example, it can briefly describe the attribute characteristics of the products reflected in the conditional data set associated with that tag. When a user has a search need, they can select the appropriate tag from the tag set to access and find the desired product.

[0057] Specifically, a product information request can be obtained from the user terminal. This product information request is used to search for the product the user needs. The product information request may include tags (e.g., adding tags) as described in this embodiment of the invention, or it may not include tags; this embodiment of the invention does not impose any limitations on this.

[0058] Furthermore, the product information request can be parsed to determine whether it contains tags from the tag set (e.g., whether it contains the newly added tags mentioned above). If so, the condition data set associated with the tags in the product information request can be determined, and the condition data set can be called to search for products that match the condition data set among multiple candidate products, so as to obtain the search results.

[0059] Taking adding a new tag as an example, if the product information request includes the aforementioned new tag, a search can be performed based on the conditional data set associated with that new tag. Here, the conditional data set associated with the new tag refers to the conditional data set that has a mapping relationship with the new tag. The specific search process can be found in the description of step S202 above, and will not be repeated here.

[0060] It's important to note that the search results obtained by accessing the newly added tag may be the same as or different from the target products found using the conditional data set associated with the newly added tag before its creation. In a specific example, the attribute data of the candidate products may change over time. For instance, the values ​​of various indicators for stocks may change over time. Therefore, searching using the same conditional data set at different times may yield different search results.

[0061] If a product information request includes multiple tags, the search can be performed separately based on the conditional data set associated with each tag. The search results can be products that match the conditional data sets associated with each of the multiple tags.

[0062] Furthermore, the search results can be fed back to the user terminal. Specifically, if a target product matching the criteria data set is found, the product information of the target product (e.g., price, logo, etc.) can be sent to the user terminal. If the search results are empty (i.e., no target product was found), a conditional search reminder can be sent to the user terminal to prompt the user to set a new criteria data set for the search. Alternatively, a tag adjustment reminder can be sent to the user to prompt the user to access and use other tags for the search, but it is not limited to these.

[0063] Furthermore, after the search results are fed back to the user terminal, the user's evaluation data on the tags can also be obtained from the user terminal. The evaluation data is the user's feedback on the search results for the tags.

[0064] Furthermore, for each tag in the tag set, its availability can be monitored after it is created. The following example illustrates this using adding a new tag.

[0065] After creating a new tag, you can also monitor its availability. Availability refers to whether enough products can be found through the tag; in other words, whether the tag is "available".

[0066] In a specific example, if the number of product information requests containing the newly added tag is less than a first preset threshold within a first preset time period after establishing the mapping relationship between the newly added tag and its associated conditional data set, the newly added tag can be deleted. Specifically, the newly added tag can be deleted from the tag set. The first preset time period can be pre-set.

[0067] In the second specific example, usability can also refer to whether the tag can find a sufficient number of the products that users expect. The usability of newly added tags can also be monitored based on user feedback data. This feedback data can be determined based on the number of target products included in the search results and the user's satisfaction with each target product.

[0068] Specifically, after establishing the mapping relationship between new tags and conditional data sets, the sharing coefficient of the new tag is calculated every second preset time interval. This sharing coefficient is calculated based on evaluation data; more specifically, it is calculated based on user feedback data regarding the new tag. In a specific example, user feedback data for the new tag can be ratings, and the sharing coefficient of the new tag can be the sum or weighted average of the ratings provided by users using the new tag.

[0069] The sharing coefficient can be used to describe the availability of the new tags. The larger the value of the sharing coefficient, the higher the availability of the new tags, indicating that the new tags can more intuitively reflect the attribute characteristics of the products reflected in the conditional data set. Users can accurately and efficiently obtain the desired product information by accessing the new tags.

[0070] Furthermore, if the sharing coefficient is less than a preset coefficient threshold, the newly added tag can be deleted. Alternatively, when the number of times the sharing coefficient is less than the preset coefficient threshold reaches a second preset threshold, the newly added tag can be deleted. The preset coefficient threshold and the second preset threshold can be pre-set.

[0071] By adopting the above scheme, the usability of new tags can be measured by calculating the sharing coefficient of new tags. By monitoring the sharing coefficient of new tags, new tags with poor usability can be deleted, which can avoid misleading users due to poor usability of new tags and improve user experience.

[0072] It should be noted that the above content only takes adding a new tag as an example. The above method can be used to monitor the availability of any tag in the tag set.

[0073] Reference Figure 3 , Figure 3 This is a partial flowchart of another tag generation method in an embodiment of the present invention. By executing steps S301 to S305, duplicate tags in the tag set can be removed to simplify and optimize the tag set. Figure 3 The method shown may include the following steps:

[0074] Step S301: Calculate attribute similarity based on the attribute types in the first condition data set and the attribute types in the second condition data set;

[0075] Step S302: Determine whether the attribute similarity is greater than the third preset threshold. If so, proceed to step S303.

[0076] Step S303: Calculate the value similarity based on the value range in the first condition data set and the value range in the second condition data set;

[0077] Step S304: Determine whether the similarity of the values ​​is greater than the fourth preset threshold. If so, proceed to step S305.

[0078] Step S305: Delete the first tag or the second tag.

[0079] In the specific implementation of step S301, any tag can be selected from the tag set as the first tag, and any tag other than the first tag can be selected from the tag set as the second tag. The condition data set associated with the first tag is the first condition data set, and the condition data set associated with the second tag is the second condition data set.

[0080] Furthermore, the similarity between the attribute types in the first conditional data set and the attribute types in the second conditional data set can be calculated and denoted as attribute similarity. This embodiment of the invention does not limit the specific method for calculating attribute similarity; it can be any existing and appropriate method for calculating similarity. For example, the cosine similarity method can be used to calculate attribute similarity, but it is not limited to this.

[0081] In the specific implementation of step S302, the attribute similarity between the first conditional data set and the second conditional data set can be compared with a third preset threshold to determine whether the attribute similarity is greater than the third preset threshold. If the attribute similarity between the first conditional data set and the second conditional data set is greater than the third preset threshold, then step S303 can be continued. The third preset threshold can be pre-set.

[0082] In the specific implementation of step S303, for the first conditional data set and the second conditional data set whose attribute similarity is greater than a third preset threshold, the value similarity between them can be calculated. Specifically, the similarity between the value range in the first conditional data set and the value range in the second conditional data set can be calculated and denoted as the value similarity. This embodiment of the invention does not limit the specific method for calculating the value similarity; it can be any existing appropriate method for calculating similarity, such as using the cosine similarity method, but is not limited to this.

[0083] In the specific implementation of step S304, the value similarity between the first conditional data set and the second conditional data set can be compared with a fourth preset threshold to determine whether the value similarity is greater than the fourth preset threshold. If the value similarity is greater than the fourth preset threshold, it can be determined that the first conditional data set and the second conditional data set have a high degree of similarity. In other words, the conditional data sets associated with the first label and the second label have a high degree of similarity. Therefore, step S305 can be further executed to delete the first label or the second label. The fourth preset threshold can be pre-set.

[0084] It should be noted that if the attribute similarity between the first and second conditional data sets is not greater than a third preset threshold, or the value similarity is not greater than a fourth preset threshold, then the first and second labels can be reselected, and the attribute and value similarities between the conditional data sets associated with the new first and second labels can be calculated. Furthermore, after executing step S305, the first and second labels can also be reselected, and the attribute and value similarities between the conditional data sets associated with the new first and second labels can be calculated. This simplifies and optimizes the label set, avoiding excessive redundancy and improving user experience.

[0085] about Figure 3 More details about the methods shown can be found in the above text. Figure 1 and Figure 2 The relevant descriptions will not be repeated here.

[0086] Reference Figure 4 , Figure 4 This is a label generation device according to an embodiment of the present invention. Figure 4 The label generation apparatus shown may include:

[0087] Condition acquisition module 41 is used to acquire a condition data set, the condition data set including at least one search condition, each search condition including an attribute type and a value range corresponding to the attribute type;

[0088] The search module 42 is used to search for a target product that matches the condition data set from multiple candidate products based on the condition data set.

[0089] The tag creation module 43 is used to create a new tag if the target product is found, and to establish a mapping relationship between the new tag and the condition data set.

[0090] In specific implementations, the aforementioned tag generation device may correspond to a chip with data processing capabilities within the terminal; or to a chip module with data processing capabilities within the terminal; or to the terminal itself.

[0091] about Figure 4 For more information on the working principle, operation, and beneficial effects of the label generation device shown, please refer to the above section on... Figures 1 to 3 The relevant descriptions will not be repeated here.

[0092] This invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, performs the steps of the above-described method. The storage medium may include ROM, RAM, a magnetic disk, or an optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0093] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it performs the steps of the above-described method. The computing device includes, but is not limited to, terminal devices such as mobile phones, computers, tablet computers, and servers.

[0094] It should be understood that in the embodiments of this application, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0095] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0096] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatus, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and other division methods may exist in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically included separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or in a combination of hardware and software functional units. For example, for various devices or products applied to or integrated into a chip, each module / unit can be implemented using hardware such as circuits, or at least some modules / units can be implemented using software programs running on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware such as circuits; for various devices or products applied to or integrated into a chip module, each module / unit can be implemented using hardware such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0099] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0100] In the embodiments of this application, "multiple" refers to two or more.

[0101] The descriptions of "first," "second," etc., appearing in the embodiments of this application are for illustrative purposes and to distinguish the objects being described. They have no order and do not indicate any special limitation on the number of devices in the embodiments of this application, nor do they constitute any limitation on the embodiments of this application.

[0102] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A label generation method characterized by, The method comprises: obtaining a condition data set, the condition data set comprising at least one search condition, each search condition comprising an attribute type and a value range corresponding to the attribute type; finding a target product matching the condition data set from a plurality of alternative products according to the condition data set; if the target product is found, creating a new label and establishing a mapping relationship between the new label and the condition data set; The method further comprises: adding the new label to a label set, the label set comprising a plurality of labels, each label in the label set being associated with a condition data set, the label being used to find a product required by a user, and the name of the label being defined by the user; obtaining a product information request from a user terminal, the product information request being used to request a product required by a user; parsing the product information request to determine whether the product information request contains a label in the label set, if so, determining a condition data set associated with the label in the product information request and calling the condition data set to find whether there is a product matching the condition data set in the plurality of alternative products to obtain a search result.

2. The label generation method according to claim 1, characterized by, The method further comprises setting the access permission of the new label to private access, the private access referring to allowing only the user setting the condition data set to access.

3. The label generation method according to claim 1, characterized by, The method further comprises publishing the new label to enable all users to access the new label.

4. The label generation method according to claim 1, characterized by, The method further comprises: if the number of product information requests containing the new label obtained within a first preset time period after establishing the mapping relationship is less than a first preset threshold, deleting the new label.

5. The label generation method according to claim 1, characterized by, The method further comprises: obtaining evaluation data of the new label from the user terminal, the evaluation data being feedback of the search result of the new label by the user; calculating a sharing coefficient of the new label every second preset time period after establishing the mapping relationship, the sharing coefficient being calculated according to the evaluation data; when the sharing coefficient is less than a preset coefficient threshold or the number of times when the sharing coefficient is less than the preset coefficient threshold reaches a second preset threshold, deleting the new label.

6. The label generation method according to claim 1, wherein The method further comprises: adding the new label to a label set, the label set comprising a plurality of labels, wherein each label has a mapping relationship with an associated condition data set; performing deduplication processing on the label set according to the condition data set associated with each label in the label set to update the label set.

7. The label generation method according to claim 6, characterized by, The deduplication processing on the label set according to the condition data set associated with each label in the label set comprises: calculating attribute similarity according to an attribute type in the first conditional data set and an attribute type in the second conditional data set, wherein the first conditional data set is a conditional data set associated with a first label, the second conditional data set is a conditional data set associated with a second label, the first label is any one of the label set, and the second label is any one of the label set except the first label; if the attribute similarity is greater than a third preset threshold, calculating value similarity according to a value range in the first conditional data set and a value range in the second conditional data set; if the value similarity is greater than a fourth preset threshold, deleting the first label or the second label.

8. A label generating apparatus characterized by comprising: The apparatus comprises: a conditional obtaining module configured to obtain a conditional data set, wherein the conditional data set comprises at least one search condition, and each search condition comprises an attribute type and a value range corresponding to the attribute type; a searching module configured to search for a target product matching the conditional data set from a plurality of candidate products according to the conditional data set; a label creating module configured to create a new label and establish a mapping relationship between the new label and the conditional data set if the target product is found. The apparatus further comprises: a module configured to add the new label to a label set, wherein the label set comprises a plurality of labels, each label in the label set is associated with a conditional data set, and the name of the label is defined by a user; a module configured to obtain a product information request from a user terminal, wherein the product information request is used to request a product required by a user; a module configured to analyze the product information request to determine whether a label in the label set is contained in the product information request, if yes, determine a conditional data set associated with the label in the product information request, and call the conditional data set to search for a product matching the conditional data set from the plurality of candidate products to obtain a search result.

9. A storage medium having stored thereon a computer program, characterized in that The computer program is run by the processor to execute the steps of the label generation method in any one of claims 1 to 7.

10. A computing device comprising a memory and a processor, said memory having stored thereon a computer program operable to run on said processor, characterized in that, The processor runs the computer program to execute the steps of the label generation method in any one of claims 1 to 7.

Images