CN111414554A - Commodity recommendation method, system, server and storage medium - Google Patents
Commodity recommendation method, system, server and storage medium Download PDFInfo
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Abstract
The application provides a commodity recommendation method, a commodity recommendation system, a server and a storage medium. The method comprises the following steps: receiving a component picture of a first commodity sent by a user terminal; acquiring text information in the component picture; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result; and sending the commodity information of the second commodity to the user terminal. Through the mode, one-stop experience from the component analysis of the commodity to the commodity recommendation is provided for the user, compared with the prior art, the operation that the user knows the components of the cosmetics is simplified, and the user experience is improved by recommending the commodity relevant to the components to the user.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, a system, a server, and a storage medium for recommending a commodity.
Background
With the increasing living standard of people, the demand of female consumers for beauty and makeup is increasing day by day. The selection criteria of consumers for cosmetic skin care products are from the early pursuit of large-brand products, and gradually trend to rationality at the stage of pursuing star advertising. Consumers are concerned about whether the purchased products are suitable for the skin and the maintenance requirements, the final foothold is the component contained in the products, and the product component also becomes the publicity center of the current cosmetic skin care products. How to know which ingredients of the cosmetic to be bought and how to search for similar ingredient products also become urgent needs of new-generation consumers. Although product ingredients are of increasing interest, consumers themselves do not have the ability to analyze cosmetic ingredients in depth. At present, the common method adopted by consumers is to copy the ingredient list from the product package one by one and input the ingredient list into a search engine, but the method is too complicated, and the consumers cannot know the product related to the ingredients.
Disclosure of Invention
The embodiment of the application aims to provide a commodity recommendation method, a commodity recommendation system, a server and a storage medium, so as to solve the problem that the process that a current consumer knows cosmetic ingredients only can be complicated by copying the ingredient table from a product package one by one and inputting the ingredient table into a search engine for query.
The invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a commodity recommendation method, including: receiving a component picture of a first commodity sent by a user terminal; acquiring text information in the component picture; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result; and sending the commodity information of the second commodity to the user terminal.
In the embodiment of the application, the method comprises the steps of receiving a component picture of a first commodity sent by a user terminal; acquiring text information in the component picture; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result; the commodity information of the second commodity is sent to the user terminal, and then a one-stop experience from commodity component analysis to commodity recommendation is provided for the user.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the acquiring text information in the component picture includes: performing OCR character recognition on the component picture to acquire text information in the component picture; performing word segmentation processing on the text information to enable each item of component in the text information to be independently arranged in a line; correspondingly, comparing the text information with data in a database comprises: and comparing each component in the text information with the data in the database separately.
In the embodiment of the application, the text information is subjected to word segmentation processing, so that each component in the text information is independently arranged in a line, the accuracy of identifying the commodity components is improved, and the subsequent effective comparison of each component in the text information and the data in the database is facilitated.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the acquiring text information in the component picture includes: performing OCR character recognition on the component picture to acquire text information in the component picture; identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information that are not related to the composition of the first item; and deleting the redundant information.
In the embodiment of the application, the redundant information in the text information is identified and deleted, so that the text information in the identified position can be conveniently compared with the data in the database subsequently, and the interference of the redundant information of the text information on the comparison result is avoided.
With reference to the technical solution provided by the first aspect, in some possible implementations, the database includes a component database and a commodity database; the comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result, includes: comparing the text information with the component data in the component database to obtain the identification of each component in the text information; and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain the second commodity associated with the first commodity.
In the embodiment of the application, the identification of each component in the text information is obtained by comparing the text information with the component data in the component database; and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain a second commodity associated with the first commodity, so that the comparison accuracy is improved.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the comparing the text information with the component data in the component database to obtain the identifier of each component in the text information includes: and comparing the text information with the component data in the component database according to a fuzzy matching algorithm to obtain the identification of each component in the text information.
In the embodiment of the application, the matching rate and the fault tolerance of component comparison can be improved through a fuzzy matching algorithm.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the comparing the identifier of each component in the text message with the commodity data in the commodity database to obtain the second commodity associated with the first commodity includes: comparing the identification of each component in the text information with the commodity data in the commodity database to obtain the matching degree of each commodity in the database and the first commodity; and sequentially acquiring the second commodities from high to low based on the matching degree.
In the embodiment of the application, the matching degree of each commodity in the database and the first commodity is obtained; and sequentially acquiring the second commodities from high to low based on the matching degree. The second commodities can be sequentially recommended to the user from high matching degree to low matching degree, and the use experience of the user is improved.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, after the obtaining the text information in the component picture, the method further includes: sending the text information to a user terminal; and receiving text information obtained after the user edits the text information based on the user terminal.
In the embodiment of the application, after the text information in the component picture is acquired, the text information is sent to the user terminal, so that the user can edit the text information according to requirements, for example, the user adds and/or deletes some components in the text information, and the use experience of the user is improved.
In a second aspect, an embodiment of the present application provides a commodity recommendation system, including: the receiving module is used for receiving the component pictures of the first commodity sent by the user terminal; the text processing module is used for acquiring text information in the component pictures; the comparison module is used for comparing the text information with data in a database and acquiring a second commodity associated with the first commodity from the database based on a comparison result; and the sending module is used for sending the commodity information of the second commodity to the user terminal.
In a third aspect, an embodiment of the present application provides a server, including: a processor and a memory, the processor and the memory connected; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory, and to perform a method as provided in the foregoing first aspect embodiment and/or in combination with some possible implementations of the foregoing first aspect embodiment; the server is provided with a database, and the database stores component data and commodity data.
In a fourth aspect, embodiments of the present application provide a storage medium having stored thereon a computer program, which, when executed by a processor, performs a method as provided in the above-described first aspect embodiment and/or in connection with some possible implementations of the above-described first aspect embodiment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a block diagram of a server according to an embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating steps of a method for recommending a commodity according to an embodiment of the present disclosure.
Fig. 3 is an effect diagram of text information after word segmentation processing according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating steps of another method for recommending merchandise according to an embodiment of the present disclosure.
Fig. 5 is a block diagram of a product recommendation system according to an embodiment of the present application.
Icon: 100-a server; 110-a processor; 120-a memory; 200-a commodity recommendation system; 201-a receiving module; 202-text processing module; 203-an alignment module; 204-sending module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
At present, consumers pay more attention to whether the purchased products are suitable for the skin and the maintenance needs of the consumers, the final foothold is the component contained in the products, and the product component also becomes the publicity center of the current cosmetic skin care products. How to know which ingredients of the cosmetic to be bought and how to search for similar ingredient products also become urgent needs of new-generation consumers. Although product ingredients are of increasing interest, consumers themselves do not have the ability to analyze cosmetic ingredients in depth. At present, the common method adopted by consumers is to copy the ingredient list from the product package one by one and input the ingredient list into a search engine, but the method is too complicated, and the consumers cannot know the product related to the ingredients.
In view of the above problems, the present inventors have studied and researched to provide the following embodiments to solve the above problems.
Referring to fig. 1, a schematic block diagram of a server 100 applying a commodity recommendation method according to an embodiment of the present application is shown. Structurally, the server 100 may include a processor 110 and a memory 120.
The processor 110 and the memory 120 are electrically connected directly or indirectly to enable data transmission or interaction, for example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 110 is used to execute the executable modules stored in the memory 120.
The processor 110 may be an integrated circuit chip having signal processing capabilities. The Processor 110 may also be a general-purpose Processor, including a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a discrete gate or transistor logic device, and discrete hardware components, which may implement or execute the methods, steps, and logic blocks disclosed in the embodiments of the present Application. Further, a general purpose processor may be a microprocessor or any conventional processor or the like.
The Memory 120 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), and an electrically Erasable Programmable Read-Only Memory (EEPROM). The memory 120 is used for storing a program, and the processor 110 executes the program after receiving the execution instruction.
Wherein, a database is also deployed in the server. The database stores component data and commodity data.
It should be understood that the structure shown in fig. 1 is merely an illustration, and the server 100 provided in the embodiment of the present application may have fewer or more components than those in fig. 1, or may have a different configuration from that shown in fig. 1. Further, the components shown in fig. 1 may be implemented by software, hardware, or a combination thereof.
Referring to fig. 2, fig. 2 is a flowchart illustrating a product recommendation method according to an embodiment of the present application, where the method is applied to the server 100 shown in fig. 1. It should be noted that, the commodity recommendation method provided in the embodiment of the present application is not limited by the order shown in fig. 2 and the following, and the method includes: step S101-step S104.
Step S101: and receiving the component picture of the first commodity sent by the user terminal.
Step S102: and acquiring text information in the component pictures.
Step S103: and comparing the text information with the data in the database, and acquiring a second commodity associated with the first commodity from the database based on the comparison result.
Step S104: and sending the commodity information of the second commodity to the user terminal.
In the embodiment of the application, the method comprises the steps of receiving a component picture of a first commodity sent by a user terminal; acquiring text information in the component picture; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result; the commodity information of the second commodity is sent to the user terminal, and then a one-stop experience from commodity component analysis to commodity recommendation is provided for the user.
The above steps are described below with reference to specific examples.
Step S101: and receiving the component picture of the first commodity sent by the user terminal.
The user may interact with the server through an APP (Application, third party Application) on the user terminal, or an applet. The user terminal may be, but is not limited to, a smart phone, a tablet computer, and the like. When a user needs to know the components of a certain commodity, an APP or an applet on a user terminal can be opened, then a component list of the commodity is shot, and finally a component picture is sent to a server.
Optionally, after the user finishes shooting the component picture, the component picture may be clipped, so that a subsequent server obtains text information related to the component in the component picture. For example, since the user photographs information such as the production date and production address of the product when photographing, the user can cut out information (production date and production address) that is not related to the component.
Optionally, the user may also directly upload the component pictures of the first product acquired in advance. For example, the user finds the component table of a certain commodity, then performs screenshot on the component table through the user terminal, and uploads the screenshot to the server as the component table.
It will be appreciated that the first item described above may be, but is not limited to, a cosmetic product, such as a beverage, food product, etc.
Step S102: and acquiring text information in the component pictures.
The text information in the component picture is acquired and can be recognized by an OCR (Optical character recognition) character recognition technology. The OCR character recognition technology refers to a process that electronic equipment checks characters printed on paper, determines the shapes of the characters by detecting dark and light modes, and then translates the shapes into computer characters by a character recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.
OCR character recognition techniques may also be used to translate recognized characters, such as chinese characters to english, chinese characters to japanese, and the like. Of course, the specific language of the text to be translated may be selected according to the user's requirement, for example, after the user uploads the component pictures through the user terminal, the obtained language type may be selected, for example, a chinese text, an english text, or a japanese text may be obtained. And after receiving the language type selected and obtained by the user terminal, the server obtains the text information of the corresponding language based on an OCR character recognition technology.
In this embodiment, the obtaining of the text information in the component picture by the OCR character recognition technology may be sending the component picture to an OCR server; and performing character recognition through an external OCR server, and then receiving text information obtained by performing character recognition on the component picture through the external OCR server. By the method, the operation pressure of the server can be reduced, and the development difficulty of the commodity recommendation system is also reduced.
Of course, in other embodiments, the server itself may perform OCR character recognition. The present application is not limited thereto.
Optionally, in order to improve the accuracy of identifying the product components and facilitate the subsequent comparison of the identified text information with the data in the database, in this embodiment, after performing OCR character recognition on the component picture and acquiring the text information in the component picture, the method further includes: the text information is subjected to word segmentation processing, so that each item in the text information is independently arranged into a line.
It should be noted that performing word segmentation processing on the text information includes performing optimization operations such as splicing and splitting on the text information. Through word segmentation processing, each component in the text information is independently arranged in a line, and effective comparison between each component in the text information and data in a database is facilitated subsequently. For example, as shown in fig. 3, each component in the text information recognized through OCR characters is separated by a comma, but when the text information is directly compared with data in a database, a comparison error is prone to occur or the text information cannot be compared with the data in the database. Therefore, word segmentation is performed by recognizing commas in text information so that each item of component occupies one line individually. It should be noted that the word segmentation also includes special cases, such as the "cetearyl" and the "olive oleate" in the "cetearyl olive oleate" before the word segmentation occupy two different rows, so that the "cetearyl" and the "olive oleate" are spliced together during the word segmentation, so that the component of the "cetearyl olive oleate" can completely occupy one row.
Optionally, after performing OCR character recognition on the component picture to obtain text information in the component picture, the method further includes: identifying redundant information in the text information; the redundant information comprises symbols, numbers and/or characters which are irrelevant to the components of the first commodity in the text information; the redundant information is deleted. For example, roman numerals may appear in the ingredient list of some commodities, which indicate the order of the ingredients, and when the roman numerals in the text information are recognized, the roman numerals in the text information are deleted. In the cosmetics industry, certain cosmetic components are marked with a symbol "", and when the "" -number "in the text message is identified, the" "-number" in the text message is deleted.
In the embodiment of the application, the redundant information in the text information is identified and deleted, so that the text information in the identified position can be conveniently compared with the data in the database subsequently, and the interference of the redundant information of the text information on the comparison result is avoided.
Optionally, after performing OCR character recognition on the component picture to obtain the text information in the component picture, the method further includes other text information optimization operations, such as format correction, tilt correction, and the like.
Optionally, after performing OCR character recognition on the component picture in the above manner to obtain text information in the component picture and processing, the method further includes: judging whether the processed text information is available, if not, sending an instruction that the current text information is unavailable to the user terminal so as to enable the user to upload the component picture again; if so, go to step S103.
It can be understood that when a user shoots a component list of a commodity, hand shaking or focus failure easily occurs, and further component pictures uploaded by the user may be unclear and fuzzy. Therefore, after the component pictures are identified and processed, if the effective components cannot be obtained, the current text information is unavailable, that is, the component pictures uploaded by the user are unavailable, and at this time, an instruction that the current text information is unavailable is sent to the user terminal, so that the user uploads the component pictures again. And after the user uploads the component pictures again through the user terminal, the step S102 is executed again to obtain the text information in the component pictures.
Referring to fig. 4, optionally, after acquiring the text information in the component picture, the method further includes: step S201-step S202.
Step S201: and sending the text information to the user terminal.
The server acquires the text information in the picture to be composed, sends the text information to the user terminal and presents the text information in front of the user. In this way, the components contained in the commodity can be preliminarily presented in front of the user.
Step S202: and receiving the text information after the user edits the text information based on the user terminal.
It is understood that after the text information is obtained, the user may edit the text information according to the requirement, such as the user modifying, adding and/or deleting some components in the text information. For example, the user may delete a component a if the user dislikes the component a in the product. Therefore, when the user edits the text information based on the user terminal. The server receives the edited text information.
Optionally, after receiving the text information edited by the user, the service performs the above-mentioned text information optimization operation on the text information, and then determines whether the text information is available again, if not, sends an instruction for re-editing to the user terminal, and if so, executes step S103.
Step S103: and comparing the text information with the data in the database, and acquiring a second commodity associated with the first commodity from the database based on the comparison result.
Wherein, the text information is compared with the data in the database, that is, the components in the text information are compared with the data in the database. For example, in step S102, the word segmentation processing is performed on the text information, so that each item of component in the text information is separately arranged in a line, and in this step, each item of component in the text information is separately compared with the data in the database.
Optionally, the database includes a composition database and a merchandise database. Comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result comprises: comparing the text information with component data in a component database to obtain the identification of each component in the text information; and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain a second commodity associated with the first commodity.
The component database is used for maintaining specific component details, including component names, component identifiers, usage purposes, component introduction, applicable groups, safety indexes and the like. The commodity database is used for maintaining specific commodity details including contents such as pictures, names, brands, classifications, manufacturers, filing information, component lists and the like. The identifier of the above-mentioned component refers to an id (unique identifier) sequence, which may be, for example, a number, an english letter, or the like. For example, when the component is panthenol, the corresponding id sequence may be 1, 2, 3, or A, B, C, and the present application is not limited thereto.
For example, if the recognized text information includes water and glycerin, the water and glycerin are compared with the component database, first, whether the component database contains water and glycerin is determined, and if the component database stores water and glycerin, the identifiers of the water and glycerin are obtained, such as the identifier of the water is 1 and the identifier of the glycerin is 4. And then comparing the component identifier obtained by the text information with the commodity data in the commodity database, for example, if the commodity A in the commodity database comprises the identifier 1 and the identifier 4, the commodity A is a second commodity associated with the first commodity.
Optionally, in order to improve the matching rate of the component comparison, the text information is compared with the component data in the component database, and the obtaining of the identifier of each component in the text information may be by comparing the text information with the component data in the component database according to a fuzzy matching algorithm to obtain the identifier of each component in the text information.
It should be noted that the fuzzy matching algorithm has a wider range of identified contents. For example, one component in the component database is "retinol", but at this time, the user mistakenly inputs "retinol" as "retinol pure" during text editing, and at this time, "retinol pure" can still be matched with "retinol" in the component database through the fuzzy matching algorithm.
In summary, the matching rate and the fault tolerance of the component comparison can be improved by the fuzzy matching algorithm.
Optionally, in order to better recommend a commodity to a user and improve the user experience of the user, the comparing the identifier of each component in the text information with the commodity data in the commodity database, and the obtaining of the second commodity associated with the first commodity includes: comparing the identification of each component in the text information with commodity data in a commodity database to obtain the matching degree of each commodity in the database and the first commodity; and sequentially acquiring the second commodities from high to low based on the matching degree.
For example, if the text information obtained in step S102 includes petrolatum, tartaric acid, and elastin, the identifiers of petrolatum, tartaric acid, and elastin, such as the identifier of petrolatum 2, the identifier of tartaric acid 3, and the identifier of elastin 7, are obtained by comparing the petrolatum, tartaric acid, and elastin with the component database. And then comparing the component identifier obtained by the text information with the commodity data in the commodity database, for example, the commodity a in the commodity database comprises an identifier 2 and an identifier 3, the commodity a and the first commodity have two identical components, and the matching degree of the commodity a and the first commodity is 2/3 × 100%, namely 66.7%. For another example, if the product B in the product database includes the identifier 2, and the product B has the same component as the first product, the matching degree between the product B and the first product is 1/3 × 100% — 33.3%. For another example, the C commodity in the commodity database includes the identifier 2, the identifier 3, and the identifier 7, and the C commodity has three same components as the first commodity, so that the matching degree of the C commodity and the first commodity is 3/3 × 100%, which is 100%, and then the second commodities are sequentially obtained based on the matching degree from high to low, that is, the C commodity, the a commodity, and the B commodity are obtained.
Step S104: and sending the commodity information of the second commodity to the user terminal.
And sending the commodity information of the second commodity to the user terminal so that the user terminal can present the recommended commodity.
Alternatively, if the second item includes the matching degree with the first item, the item with the matching degree of the fifth item may be sent to the user terminal, or only the item with the matching degree of the third item may be sent to the user terminal.
In summary, in the embodiment of the present application, a component picture of a first commodity sent by a user terminal is received; acquiring text information in the component picture; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result; the commodity information of the second commodity is sent to the user terminal, and then a one-stop experience from commodity component analysis to commodity recommendation is provided for the user.
Next, a description will be given of a product recommendation method provided in the embodiments of the present application by way of a complete example. Firstly, a user opens an applet of a user terminal, then opens a shooting function, shoots a component list of a first commodity, and sends a component picture to a server. After the server acquires the component picture, the component picture is sent to an OCR server; and performing character recognition through an external OCR server, and then receiving text information obtained by performing character recognition on the component picture through the external OCR server. The server then performs text information optimization operations on the text information, such as word segmentation processing, redundant information deletion, format correction and skew correction. And after optimization, judging whether the text information after optimization processing is available, if not, sending an instruction that the current text information is unavailable to the user terminal so as to enable the user to upload the component picture again. If the text information can be sent to the user terminal, the user can edit the text information according to requirements, then the server receives the text information obtained after the user edits the text information based on the user terminal, and then the text information optimization operation is carried out on the text information. Then judging whether the text information is available again, if not, sending a re-editing instruction to the user terminal, if so, comparing the text information with the component data in the component database to obtain the identification of each component in the text information; and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain a second commodity associated with the first commodity. And finally, sending the commodity information of the second commodity to the user terminal so that the user terminal can present the recommended commodity.
Referring to fig. 5, based on the same inventive concept, an embodiment of the present application further provides a product recommendation system 200, including: a receiving module 201, a text processing module 202, a comparing module 203 and a sending module 204.
The receiving module 201 is configured to receive a component picture of a first commodity sent by a user terminal.
And the text processing module 202 is configured to obtain text information in the component picture.
A comparison module 203, configured to compare the text information with data in a database, and obtain, based on a comparison result, a second product associated with the first product from the database.
A sending module 204, configured to send the commodity information of the second commodity to the user terminal.
Optionally, the text processing module 202 is specifically configured to perform OCR character recognition on the component picture to obtain text information in the component picture; performing word segmentation processing on the text information so as to enable each item of component in the text information to be arranged in a line independently. Correspondingly, the comparison module 203 is configured to separately compare each component in the text message with the data in the database.
Optionally, the text processing module 202 is specifically configured to perform OCR character recognition on the component picture to obtain text information in the component picture; identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information that are not related to the composition of the first item; and deleting the redundant information.
Optionally, the database includes a component database and a commodity database, and the comparison module 203 is specifically configured to compare the text information with component data in the component database to obtain an identifier of each component in the text information; and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain the second commodity associated with the first commodity.
Optionally, the comparing module 203 is specifically configured to compare the text information with the component data in the component database according to a fuzzy matching algorithm, and obtain an identifier of each component in the text information.
Optionally, the comparison module 203 is specifically configured to compare the identifier of each component in the text message with the commodity data in the commodity database, and obtain a matching degree between each commodity in the database and the first commodity; and sequentially acquiring the second commodities from high to low based on the matching degree.
Optionally, the sending module 204 is further configured to send the text information to the user terminal after the text information in the component picture is obtained. The receiving module 201 is further configured to receive text information obtained by text editing of the text information by a user based on a user terminal after the text information is sent to the user terminal.
It should be noted that, as those skilled in the art can clearly understand, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Based on the same inventive concept, the present application further provides a storage medium, on which a computer program is stored, and when the computer program is executed, the computer program performs the method provided in the foregoing embodiments.
The storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method 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 logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for recommending an article, comprising:
receiving a component picture of a first commodity sent by a user terminal;
acquiring text information in the component picture;
comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result;
and sending the commodity information of the second commodity to the user terminal.
2. The method for recommending a commodity according to claim 1, wherein said obtaining text information in said component picture comprises:
performing OCR character recognition on the component picture to acquire text information in the component picture;
performing word segmentation processing on the text information to enable each item of component in the text information to be independently arranged in a line;
correspondingly, comparing the text information with data in a database comprises:
and comparing each component in the text information with the data in the database separately.
3. The method for recommending a commodity according to claim 1, wherein said obtaining text information in said component picture comprises:
performing OCR character recognition on the component picture to acquire text information in the component picture;
identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information that are not related to the composition of the first item;
and deleting the redundant information.
4. The merchandise recommendation method according to claim 1, wherein the database includes a composition database and a merchandise database; the comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on a comparison result, includes:
comparing the text information with the component data in the component database to obtain the identification of each component in the text information;
and comparing the identification of each component in the text information with the commodity data in the commodity database to obtain the second commodity associated with the first commodity.
5. The commodity recommendation method according to claim 4, wherein the comparing the text information with the component data in the component database to obtain the identifier of each component in the text information comprises:
and comparing the text information with the component data in the component database according to a fuzzy matching algorithm to obtain the identification of each component in the text information.
6. The method for recommending a commodity according to claim 4, wherein the step of comparing the identifier of each component in the text message with the commodity data in the commodity database to obtain the second commodity associated with the first commodity comprises:
comparing the identification of each component in the text information with the commodity data in the commodity database to obtain the matching degree of each commodity in the database and the first commodity;
and sequentially acquiring the second commodities from high to low based on the matching degree.
7. The item recommendation method according to claim 1, wherein after said acquiring text information in said component picture, said method further comprises:
sending the text information to a user terminal;
and receiving text information obtained after the user edits the text information based on the user terminal.
8. An article recommendation system, comprising:
the receiving module is used for receiving the component pictures of the first commodity sent by the user terminal;
the text processing module is used for acquiring text information in the component pictures;
the comparison module is used for comparing the text information with data in a database and acquiring a second commodity associated with the first commodity from the database based on a comparison result;
and the sending module is used for sending the commodity information of the second commodity to the user terminal.
9. A server, comprising: a processor and a memory, the processor and the memory connected;
the memory is used for storing programs;
the processor is configured to execute a program stored in the memory to perform the method of any of claims 1-7;
the server is provided with a database, and the database stores component data and commodity data.
10. A storage medium, having stored thereon a computer program which, when executed by a computer, performs the method of any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569058A (en) * | 2021-08-05 | 2021-10-29 | 武汉美之修行信息科技有限公司 | Information query method and device and computer readable storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009143109A1 (en) * | 2008-05-21 | 2009-11-26 | Zeer, Inc. | Interest-based shopping lists and coupons for networked devices |
| CN104424201A (en) * | 2013-08-21 | 2015-03-18 | 富士通株式会社 | Method and device for providing food safety information |
| CN105809479A (en) * | 2016-03-07 | 2016-07-27 | 海信集团有限公司 | Item recommending method and device |
| CN105956412A (en) * | 2016-04-22 | 2016-09-21 | 北京阳光欣晴健康管理有限责任公司 | System and method for realizing coronary heart disease clinical data collection based on intelligent image-text identification |
| CN107123027A (en) * | 2017-04-28 | 2017-09-01 | 广东工业大学 | A kind of cosmetics based on deep learning recommend method and system |
| CN108537566A (en) * | 2018-01-30 | 2018-09-14 | 深圳市阿西莫夫科技有限公司 | Commodity selling method, device and the cosmetics shelf of cosmetics shelf |
| CN110245217A (en) * | 2019-06-17 | 2019-09-17 | 京东方科技集团股份有限公司 | A kind of drug recommended method, device and electronic equipment |
| CN110391009A (en) * | 2019-06-11 | 2019-10-29 | 拉扎斯网络科技（上海）有限公司 | Method, device, server and storage medium for analyzing nutrient components |
| US20200019597A1 (en) * | 2018-07-12 | 2020-01-16 | Kenneth Leeser | Providing Nutritional Information From Recipe Images |
-
2020
- 2020-03-26 CN CN202010227566.3A patent/CN111414554B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009143109A1 (en) * | 2008-05-21 | 2009-11-26 | Zeer, Inc. | Interest-based shopping lists and coupons for networked devices |
| CN104424201A (en) * | 2013-08-21 | 2015-03-18 | 富士通株式会社 | Method and device for providing food safety information |
| CN105809479A (en) * | 2016-03-07 | 2016-07-27 | 海信集团有限公司 | Item recommending method and device |
| CN105956412A (en) * | 2016-04-22 | 2016-09-21 | 北京阳光欣晴健康管理有限责任公司 | System and method for realizing coronary heart disease clinical data collection based on intelligent image-text identification |
| CN107123027A (en) * | 2017-04-28 | 2017-09-01 | 广东工业大学 | A kind of cosmetics based on deep learning recommend method and system |
| CN108537566A (en) * | 2018-01-30 | 2018-09-14 | 深圳市阿西莫夫科技有限公司 | Commodity selling method, device and the cosmetics shelf of cosmetics shelf |
| US20200019597A1 (en) * | 2018-07-12 | 2020-01-16 | Kenneth Leeser | Providing Nutritional Information From Recipe Images |
| CN110391009A (en) * | 2019-06-11 | 2019-10-29 | 拉扎斯网络科技（上海）有限公司 | Method, device, server and storage medium for analyzing nutrient components |
| CN110245217A (en) * | 2019-06-17 | 2019-09-17 | 京东方科技集团股份有限公司 | A kind of drug recommended method, device and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569058A (en) * | 2021-08-05 | 2021-10-29 | 武汉美之修行信息科技有限公司 | Information query method and device and computer readable storage medium |
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|---|---|
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