CN111414554B - Commodity recommendation method, commodity recommendation system, server and storage medium - Google Patents
Commodity recommendation method, commodity recommendation 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 pictures; 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. By the method, one-stop experience from component analysis of the commodity to commodity recommendation is provided for the user, compared with the prior art, the operation of knowing the components of the cosmetics by the user is simplified, and the user experience is improved by recommending the commodity related to the components to the user.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a commodity recommendation method, a commodity recommendation system, a server and a storage medium.
Background
With the increasing level of living of people, the demand of female consumers for beauty and make-up is increasing. The selection criteria of cosmetic skin care products by consumers have now gradually tended to be rational, starting from the early pursuit of large-brand products, through the stage of pursuing the star advertisement statement. Consumers are now more concerned about whether the purchased product is suitable for their skin or for their maintenance, and all of these last foothold points are the components contained in the product, which also becomes the propaganda center for current cosmetic skin care products. How to know which ingredients are present in the purchased and purchased cosmetics and how to find similar ingredient products is also becoming an urgent need for new generation consumers. While product ingredients are increasingly interesting, consumers themselves do not have the ability to analyze cosmetic ingredients deeply. At present, a consumer often uses a method of reading and writing a component list from a product package one by one and inputting the component list into a search engine, but the method is complicated, and the consumer cannot learn the product related to the component.
Disclosure of Invention
The embodiment of the application aims to provide a commodity recommending method, a commodity recommending system, a server and a storage medium, so as to solve the problem that the current process of knowing the components of cosmetics can only be read from a product package one by a consumer and inputting a search engine for inquiring is complicated.
The application is realized in the following way:
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 pictures; 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 component picture of the first commodity sent by the user terminal is received; acquiring text information in the component pictures; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on the comparison result; and the commodity information of the second commodity is sent to the user terminal, so that one-stop experience from component analysis of the commodity to commodity recommendation is provided for the user.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the obtaining text information in the component picture includes: performing OCR text recognition on the component pictures to obtain text information in the component pictures; word segmentation is carried out on the text information, so that each item of component in the text information is independently arranged in one row; correspondingly, comparing the text information with the data in the database comprises the following steps: and comparing each component in the text information with the data in the database separately.
In the embodiment of the application, the word segmentation processing is carried out on the text information, so that each item of component in the text information is independently arranged in one row, the accuracy of identifying commodity components is improved, and the effective comparison of each item of component in the text information and the data in the database is facilitated.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the obtaining text information in the component picture includes: performing OCR text recognition on the component pictures to obtain text information in the component pictures; identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information which are irrelevant to the composition of the first commodity; and deleting the redundant information.
In the embodiment of the application, the redundant information in the text information is identified, and the redundant information is deleted, so that the text information in the identification position can be conveniently compared with the data in the database, and the interference of the redundant information of the text information on the comparison result is avoided.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the database includes a component database and a commodity 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, wherein the method comprises the following steps: 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 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 commodity data in a commodity database to obtain a second commodity associated with the first commodity, thereby improving the accuracy of comparison.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the comparing the text information with component data in the component database to obtain identifiers 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 the component ratio pair can be improved through the fuzzy matching algorithm.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the 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 includes: comparing the identification of each component in the text information with 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 based on the sequence of the matching degree from high to low.
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 based on the sequence of the matching degree from high to low. The second commodities can be sequentially recommended to the user from high to low in the follow-up matching degree, and the use experience of the user is improved.
With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, after the obtaining text information in the component picture, the method further includes: the text information is sent to a user terminal; and receiving text information after the user carries out text editing on 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 the requirement, 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: the device comprises a processor and a memory, wherein the processor is connected with the memory; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory and perform a method as provided by the embodiments of the first aspect and/or in combination with some possible implementations of the embodiments of the first aspect; wherein, 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 by the embodiments of the first aspect described above and/or in connection with some possible implementations of the embodiments of the first aspect described above.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed 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 should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a server according to an embodiment of the present application.
Fig. 2 is a flowchart of steps of a commodity recommendation method according to an embodiment of the present application.
Fig. 3 is an effect diagram of text information after word segmentation according to an embodiment of the present application.
Fig. 4 is a flowchart illustrating steps of another commodity recommendation method according to an embodiment of the present application.
Fig. 5 is a block diagram of a commodity recommendation system according to an embodiment of the present application.
Icon: 100-server; 110-a processor; 120-memory; 200-commodity recommendation system; 201-a receiving module; 202-a text processing module; 203-comparing the modules; 204-a transmitting module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
At present, consumers are more concerned about whether the purchased product is suitable for the skin or the maintenance requirement of the consumers, and all the last foothold points are components contained in the product, and the product components become propaganda centers of the current cosmetic skin care products. How to know which ingredients are present in the purchased and purchased cosmetics and how to find similar ingredient products is also becoming an urgent need for new generation consumers. While product ingredients are increasingly interesting, consumers themselves do not have the ability to analyze cosmetic ingredients deeply. At present, a consumer often uses a method of reading and writing a component list from a product package one by one and inputting the component list into a search engine, but the method is complicated, and the consumer cannot learn the product related to the component.
In view of the above problems, the present inventors have studied and studied, and have proposed the following examples 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 is electrically connected to the memory 120, either directly or indirectly, to enable data transmission or interaction, for example, the elements may be electrically connected to each other via one or more communication buses or signal lines. The processor 110 is configured to execute executable modules stored in the memory 120.
The processor 110 may be an integrated circuit chip with signal processing capability. The processor 110 may also be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), discrete gate or transistor logic, discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. Further, the general purpose processor may be a microprocessor or any conventional processor or the like.
The Memory 120 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), and electrically erasable programmable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM). The memory 120 is used for storing a program, and the processor 110 executes the program after receiving an execution instruction.
Wherein, the server is also provided with a database. The database stores component data and commodity data.
It should be understood that the configuration shown in fig. 1 is merely illustrative, and that the server 100 provided in the embodiment of the present application may have fewer or more components than those shown in fig. 1, or may have a different configuration than those shown in fig. 1. In addition, 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 commodity 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 to the sequence shown in fig. 2 and the following, and the method includes: step S101 to 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 commodity information of the second commodity to the user terminal.
In the embodiment of the application, the component picture of the first commodity sent by the user terminal is received; acquiring text information in the component pictures; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on the comparison result; and the commodity information of the second commodity is sent to the user terminal, so that one-stop experience from component analysis of the commodity to commodity recommendation is provided for the user.
The above steps are described below in connection with 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) or applet on the user terminal. The user terminal may be, but is not limited to, a smart phone, a tablet computer, etc. When the user needs to know the composition of a commodity, the APP or the applet on the user terminal can be opened, then the composition table of the commodity is shot, and finally the composition picture is sent to the server.
Optionally, after the user finishes shooting the component picture, the user may cut the component picture, so that the subsequent server obtains text information related to the component in the component picture. For example, when a user shoots, the user shoots information such as the date of production and the address of production of a commodity, and therefore, the user can remove information (date of production and address of production) that is not related to the components by cutting.
Alternatively, the user may directly upload the component picture of the first commodity acquired in advance. For example, a user searches a component table of a commodity, then captures a component table through a user terminal, and uploads the captured image as a component picture to a server.
It is understood that the first commodity may be, but is not limited to, a cosmetic product, such as a beverage, a food, etc.
Step S102: and acquiring text information in the component pictures.
The text information in the component pictures 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 shape of the characters by detecting dark and bright modes, and then translates the shape into computer characters by a character recognition method; that is, the technology of converting the characters in the paper document into the image file of black-white lattice by optical mode and converting the characters in the image into the text format by the recognition software for further editing and processing by the word processing software is adopted.
OCR text recognition techniques may also be used to translate recognized text, such as translating chinese text into english, translating chinese text into japanese, and so on. Of course, the text translated into which language can be selected according to the user's requirement, for example, after the user uploads the composition picture through the user terminal, the acquired language type can be selected, for example, the chinese text, the english text or the japanese text can be acquired. After the server receives the language types selected and acquired by the user terminal, text information of the corresponding language is acquired based on the OCR text recognition technology.
In this embodiment, the text information in the component picture may be obtained by using an OCR text recognition technique, where the component picture is sent to an OCR server; and performing character recognition through an external OCR server, and then receiving text information after the external OCR server performs character recognition on the component pictures. 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 word recognition. The present application is not limited to this.
Optionally, in order to improve accuracy of recognition of the commodity component, it is convenient to compare the recognized text information with data in a database, and in this embodiment, after OCR text recognition is performed on the component picture, the method further includes: the text information is subjected to word segmentation processing so that each item of component in the text information is arranged in a single line.
It should be noted that, word segmentation processing on text information includes optimizing operations such as splicing and splitting the text information. Through word segmentation processing, each item of component in the text information is arranged in a row independently, so that the subsequent effective comparison of each item of component in the text information and data in a database is facilitated. The word segmentation processing may be to segment the text information by identifying punctuation marks in the text information, for example, as shown in fig. 3, each item of component in the text information after OCR word recognition is separated by commas, but the text information is directly compared with data in a database to easily generate comparison errors or situations that cannot be compared. Therefore, word segmentation is performed by recognizing commas in text information such that each item of component occupies one line individually. It should be noted that the word segmentation process also includes special cases, such as that "cetostearyl" and "oleyl oleate" in "cetostearyl oleate" occupy two different rows respectively before word segmentation, so that the "cetostearyl" and "oleyl oleate" are spliced during word segmentation, so that the component "cetostearyl oleate" can completely occupy one row.
Optionally, after performing OCR text recognition on the component picture to obtain text information in the component picture, the method further includes: identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information which are irrelevant to the composition of the first commodity; and deleting the redundant information. For example, roman numerals may appear in the component list of some goods, which indicates the order of the components, and when the roman numerals in the text information are identified, the roman numerals in the text information are deleted. In the cosmetic industry, certain cosmetic ingredients are preceded by the symbol "+", and when the "+", in the text message is identified, the "+", in the text message is deleted.
In the embodiment of the application, the redundant information in the text information is identified, and the redundant information is deleted, so that the text information in the identification position can be conveniently compared with the data in the database, and the interference of the redundant information of the text information on the comparison result is avoided.
Optionally, after performing OCR text recognition on the component picture to obtain 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 word recognition on the component picture in the above manner and obtaining text information in the component picture, the method further includes: judging whether the processed text information is available or not, 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 pictures again; if available, step S103 is performed.
It can be understood that when the user shoots the composition table of the commodity, the user easily shakes hands or does not focus, and therefore the composition picture uploaded by the user may be unclear and the text is blurred. Therefore, if effective components still cannot be obtained after the component pictures are identified and processed, the current text information is not available, namely, the component pictures uploaded by the user are not available, and at the moment, an instruction that the current text information is not available is sent to the user terminal so that the user can upload the component pictures again. When the user re-uploads the component picture through the user terminal, step S102 is re-executed to obtain the text information in the component picture.
Referring to fig. 4, optionally, after obtaining the text information in the component picture, the method further includes: step S201 to step S202.
Step S201: and sending the text information to the user terminal.
The server acquires text information in the picture to be composed, sends the text information to the user terminal and displays the text information in front of the user. In this way, the components contained in the commodity can be presented initially in front of the user.
Step S202: and receiving the text information after the user performs text editing on the text information based on the user terminal.
It will be appreciated that after the text information is obtained, the user may also edit the text information as desired, such as by modifying, adding and/or deleting some components in the text information. For example, the user may delete the component a in the product, while disliked the component a. Therefore, after the user performs text editing on 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 text information optimizing operation on the text information, and then determines whether the text information is available again, if not, then sends a re-editing instruction to the user terminal, and if so, step S103 is performed.
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 comparison of the text information with the data in the database is performed, i.e. the composition of the text information is compared with the data in the database. For example, by performing word segmentation processing on the text information in step S102 so that each item of component in the text information is individually set in one line, in this step, each item of component in the text information is individually compared with data in the database.
Optionally, the database comprises a component database and a commodity database. Comparing the text information with the data in the database, and based on the comparison result, acquiring the second commodity associated with the first commodity from the database comprises: 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 commodity data in a 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, application purposes, component introduction, applicable crowd, safety indexes and the like. The commodity database is used for maintaining specific commodity details, including contents such as pictures, names, brands, classifications, factories, record information, component tables and the like. The identification of the above components refers to an id (Identity document, unique identification) sequence, which may be numbers, english letters, etc. For example, when the component is panthenol, the corresponding id sequences may be 1, 2, 3, or A, B, C, which is not limited by the present application.
The comparison process is described below by way of example, for example, if the identified text information includes water and glycerin, the water and glycerin are compared with the component database, first, it is determined whether the component database has water and glycerin, and if the component database has water and glycerin stored therein, the identification of water and glycerin is obtained, for example, the identification of water is 1, and the identification of glycerin is 4. And comparing the component identification obtained by the text information with commodity data in a commodity database, wherein if the commodity A in the commodity database comprises the identification 1 and the identification 4, the commodity A is a second commodity associated with the first commodity.
Optionally, in order to improve the matching rate of the component ratio pairs, the text information is compared with the component data in the component database, and the identification of each component in the text information is obtained by comparing the text information with the component data in the component database according to a fuzzy matching algorithm.
It should be noted that the content identified by the fuzzy matching algorithm is more extensive. For example, one component in the component database is "retinol", but at this time, when the user inputs "retinol" into "retinol" by mistake during text editing, the "retinol" can still be matched with "retinol" in the component database by the fuzzy matching algorithm.
In summary, the matching rate and the fault tolerance of the component ratio pairs can be improved through the fuzzy matching algorithm.
Optionally, in order to better recommend a commodity to the user, improve the use experience of the user, comparing the identifier of each component in the text information with the commodity data in the commodity database, and acquiring 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 based on the sequence of the matching degree from high to low.
In the following, for example, the text information obtained in step S102 includes petrolatum, fruit acid and elastin, and the petrolatum, fruit acid and elastin are compared with the composition database, and the marks of petrolatum, fruit acid and elastin, for example, the mark of petrolatum is 2, the mark of fruit acid is 3 and the mark of elastin is 7 are obtained. And comparing the component identifications obtained by the text information with commodity data in a commodity database, wherein for example, an A commodity in the commodity database comprises an identification 2 and an identification 3, and the A commodity and the first commodity have two identical components, so that the matching degree of the A commodity and the first commodity is 2/3 x 100% = 66.7%. For example, if the B commodity in the commodity database includes the identifier 2 and the B commodity has the same component as the first commodity, the matching degree between the B commodity and the first commodity is 1/3×100% =33.3%. For example, the C commodity in the commodity database includes a identifier 2, a identifier 3 and a identifier 7, and the C commodity and the first commodity have three identical components, so that the matching degree of the C commodity and the first commodity is 3/3 x 100% =100%, and then the second commodity is sequentially obtained based on the sequence of 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 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 presents the recommended commodity.
Optionally, if the second commodity includes a matching degree with the first commodity, the commodity with the first matching degree may be sent to the user terminal, or only the commodity with the first matching degree 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 pictures; comparing the text information with data in a database, and acquiring a second commodity associated with the first commodity from the database based on the comparison result; and the commodity information of the second commodity is sent to the user terminal, so that one-stop experience from component analysis of the commodity to commodity recommendation is provided for the user.
The commodity recommendation method provided by the embodiment of the present application will be described with a complete example. Firstly, a user opens an applet of a user terminal, then opens a shooting function, shoots a composition table of a first commodity, and sends a composition picture to a server. After the server acquires the component pictures, the server sends the component pictures to an OCR server; and performing character recognition through an external OCR server, and then receiving text information after the external OCR server performs character recognition on the component pictures. The server performs text information optimizing operation such as word segmentation processing, redundant information deleting, format correcting and inclination correcting on the text information. After the optimization, judging whether the text information after the optimization is available or not, 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 composition picture again. If the text information can be sent to the user terminal, the user can edit the text information according to the requirement, then the server receives the text information after the user edits the text information based on the user terminal, and then the text information optimizing 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, and 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 commodity data in a commodity database to obtain a second commodity associated with the first commodity. And finally, sending commodity information of the second commodity to the user terminal so that the user terminal presents the recommended commodity.
Referring to fig. 5, based on the same inventive concept, an embodiment of the present application further provides a commodity recommendation system 200, including: a receiving module 201, a text processing module 202, a comparing module 203 and a transmitting module 204.
And the receiving module 201 is used for receiving the component picture of the first commodity sent by the user terminal.
The text processing module 202 is configured to obtain text information in the component picture.
And the comparison module 203 is configured to compare the text information with data in a database, and obtain, from the database, a second commodity associated with the first commodity based on a comparison result.
And the sending module 204 is 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 text recognition on the component picture, and obtain text information in the component picture; and performing word segmentation processing on the text information so that each item of component in the text information is independently arranged in one row. Accordingly, the comparison module 203 is configured to compare each component in the text information with the data in the database separately.
Optionally, the text processing module 202 is specifically configured to perform OCR text recognition on the component picture, and 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 which are irrelevant to the composition of the first commodity; 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, and obtain an identifier of each component in the text information; and comparing the identification of each component in the text information with commodity data in the commodity database to obtain the second commodity associated with the first commodity.
Optionally, the comparison module 203 is specifically configured to compare the text information with the component data in the component database according to a fuzzy matching algorithm, so as to obtain the 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 information with the commodity data in the commodity database, so as to obtain the matching degree between each commodity in the database and the first commodity; and sequentially acquiring the second commodities based on the sequence of the matching degree from high to low.
Optionally, the sending module 204 is further configured to send the text information to a user terminal after the text information in the component picture is acquired. The receiving module 201 is further configured to receive, after the text information is sent to a user terminal, text information obtained by text editing of the text information by a user based on the user terminal.
It should be noted that, since it will be clearly understood by those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, apparatuses and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
Based on the same inventive concept, the embodiments of the present application also provide a storage medium having stored thereon a computer program which, when executed, performs the method provided in the above embodiments.
The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. 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)), etc.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
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 variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A commodity recommendation method, comprising:
receiving a component picture of a first commodity sent by a user terminal;
acquiring text information in the component pictures;
the text information is sent to a user terminal;
receiving text information after a user carries out text editing on the text information based on the user terminal;
comparing the text information after text editing 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 commodity recommendation method according to claim 1, wherein said obtaining text information in said component pictures comprises:
performing OCR text recognition on the component pictures to obtain text information in the component pictures;
word segmentation is carried out on the text information, so that each item of component in the text information is independently arranged in one row;
correspondingly, comparing the text information with the data in the database comprises the following steps:
and comparing each component in the text information with the data in the database separately.
3. The commodity recommendation method according to claim 1, wherein said obtaining text information in said component pictures comprises:
performing OCR text recognition on the component pictures to obtain text information in the component pictures;
identifying redundant information in the text information; wherein the redundant information comprises symbols, numbers and/or words in the text information which are irrelevant to the composition of the first commodity;
and deleting the redundant information.
4. The merchandise recommendation method according to claim 1, wherein the database comprises 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, wherein the method comprises the following steps:
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 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 said comparing the text information with the component data in the component database to obtain the identifications of the respective components 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.
6. The commodity recommendation method according to claim 4, wherein said comparing the identification of each component in the text information with 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 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 based on the sequence of the matching degree from high to low.
7. A merchandise 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 sending module is used for sending the text information to the user terminal;
the receiving module is further used for receiving text information after the user edits the text information based on the user terminal;
the comparison module is used for comparing the text information after text editing 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 also used for sending the commodity information of the second commodity to the user terminal.
8. A server, comprising: the device comprises a processor and a memory, wherein the processor is connected with the memory;
the memory is used for storing programs;
the processor being configured to execute a program stored in the memory, performing the method of any one of claims 1-6;
wherein, the server is provided with a database, and the database stores component data and commodity data.
9. A storage medium having stored thereon a computer program which, when run by a computer, performs the method of any of claims 1-6.
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