CN111428159A - Online classification method and device - Google Patents
Online classification method and device Download PDFInfo
- Publication number
- CN111428159A CN111428159A CN202010187129.3A CN202010187129A CN111428159A CN 111428159 A CN111428159 A CN 111428159A CN 202010187129 A CN202010187129 A CN 202010187129A CN 111428159 A CN111428159 A CN 111428159A
- Authority
- CN
- China
- Prior art keywords
- classification
- data
- classified
- target
- product
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses an online classification method and device, and relates to the technical field of computers. One embodiment of the method comprises: receiving a product classification request, and generating an object to be classified corresponding to a target product according to the product classification request; setting a tested recruitment standard corresponding to a target product, and selecting a target tested corresponding to the target product according to the tested recruitment standard; and obtaining the classification operation data of the object to be classified of the target, analyzing the classification operation data and obtaining a classification result. According to the implementation mode, online classification can be achieved, the problems of data interference and condition limitation caused by a traditional card are solved, the classification is combined with user characteristics to obtain an optimal result, and the classification efficiency is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for online classification.
Background
With the advent of the information-oriented era, operators are continuously pushing out diversified internet products in order to meet user demands. Because the internet products are high in updating speed and competitive, how to classify the functions or menus of the internet products improves the core competitiveness of the internet products has important research significance.
The function module division and menu classification of the internet products by adopting the traditional card classification method are realized by the following steps: firstly, manufacturing a functional module or a menu into a paper card; then inviting a specific tested to a spacious and private place, and testing to classify the cards at a relatively fixed time; and finally, simply recording the classification behaviors of the tested subjects by researchers, interviewing after the classification is finished, and analyzing the final classification data.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the traditional card classification method has more conditions, such as field, time and other conditions, and the process is complicated; the artificial participation of researchers causes more influence factors and data interference; currently, data analysis is carried out on the final classification result, the need mining and the understanding of the psychological model to be tested are not deep enough, and the accuracy of the final result is reduced.
Disclosure of Invention
In view of this, embodiments of the present invention provide an online classification method and apparatus, which can implement online classification, solve the problem of data interference and the problem of condition limitation caused by the conventional card, and enable classification to combine with user features to obtain an optimal result, thereby improving classification efficiency.
To achieve the above object, according to a first aspect of embodiments of the present invention, a method for linearized classification is provided.
The online classification method of the embodiment of the invention comprises the following steps: receiving a product classification request, and generating an object to be classified corresponding to a target product according to the product classification request; setting a subject recruitment standard corresponding to the target product, and selecting a target subject corresponding to the target product according to the subject recruitment standard; and obtaining the classification operation data of the target object to be classified, analyzing the classification operation data and obtaining a classification result.
Optionally, the generating an object to be classified corresponding to a target product according to the product classification request includes: according to the product classification request, obtaining attribute information corresponding to the target product, wherein the attribute information comprises at least one of the following options: function information, menu information, spatial element information; and inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating the object to be classified.
Optionally, after receiving the product classification request, the method further comprises: and setting a classification rule corresponding to the target product according to the product classification request.
Optionally, the obtaining of the classification operation data of the target object to be classified includes: sending the object to be classified and the classification rule to the target object to be tested according to a preset form; and acquiring the classification operation data after the target object is tried to operate the object to be classified based on the classification rule.
Optionally, the classification rule includes a classification category; and the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: the target is tried to add the object to be classified to the classification category.
Optionally, the classification rule does not include a classification category; and the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: and the target is tested to define the classification category by self, and then the object to be classified is added into the defined classification category.
Optionally, the sorting operation data includes: classification result data and classification process data; and analyzing the classification operation data to obtain a classification result, wherein the classification result comprises: analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data; analyzing the classified process data to obtain second analysis data corresponding to the classified process data, wherein the classified process data comprise: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data; and correcting the first analysis data by using the second analysis data to generate a classification framework mode and a classification category corresponding to the object to be classified.
Optionally, the setting of the candidate recruitment criterion corresponding to the target product, and selecting the target candidate corresponding to the target product according to the candidate recruitment criterion includes: setting a tested recruitment standard corresponding to the target product according to the product classification request; generating a to-be-recruited questionnaire according to the to-be-recruited standard, and sending the to-be-recruited questionnaire to a user in a preset form; acquiring feedback data of the user on the interviewed recruited questionnaire; selecting the target subject from the user according to the feedback data and the subject recruitment criteria.
Optionally, the preset form comprises at least one of the following options: two-dimensional code, bar code, web page link.
To achieve the above object, according to a second aspect of the embodiments of the present invention, there is provided a linearized classification apparatus.
The online classification device of the embodiment of the invention comprises: the generation module is used for receiving a product classification request and generating an object to be classified corresponding to a target product according to the product classification request; the selection module is used for setting a to-be-tested recruitment standard corresponding to the target product and selecting a target to-be-tested corresponding to the target product according to the to-be-tested recruitment standard; and the analysis module is used for acquiring the classification operation data of the target to be tested on the object to be classified, analyzing the classification operation data and obtaining a classification result.
Optionally, the generating module is further configured to: according to the product classification request, obtaining attribute information corresponding to the target product, wherein the attribute information comprises at least one of the following options: function information, menu information, spatial element information; and inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating the object to be classified.
Optionally, the generating module is further configured to: and setting a classification rule corresponding to the target product according to the product classification request.
Optionally, the analysis module is further configured to: sending the object to be classified and the classification rule to the target object to be tested according to a preset form; and acquiring the classification operation data after the target object is tried to operate the object to be classified based on the classification rule.
Optionally, the classification rule includes a classification category; and the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: the target is tried to add the object to be classified to the classification category.
Optionally, the classification rule does not include a classification category; and the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: and the target is tested to define the classification category by self, and then the object to be classified is added into the defined classification category.
Optionally, the sorting operation data includes: classification result data and classification process data; and the analysis module is further configured to: analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data; analyzing the classified process data to obtain second analysis data corresponding to the classified process data, wherein the classified process data comprise: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data; and correcting the first analysis data by using the second analysis data to generate a classification framework mode and a classification category corresponding to the object to be classified.
Optionally, the selection module is further configured to: setting a tested recruitment standard corresponding to the target product according to the product classification request; generating a to-be-recruited questionnaire according to the to-be-recruited standard, and sending the to-be-recruited questionnaire to a user in a preset form; acquiring feedback data of the user on the interviewed recruited questionnaire; selecting the target subject from the user according to the feedback data and the subject recruitment criteria.
Optionally, the preset form comprises at least one of the following options: two-dimensional code, bar code, web page link.
To achieve the above object, according to a third aspect of embodiments of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the online classification method of the embodiment of the invention.
To achieve the above object, according to a fourth aspect of embodiments of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention has stored thereon a computer program which, when executed by a processor, implements the online classification method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: can online receipt product classification request, directly generate the object of treating classification according to classification request, solve the data interference problem that traditional card brought, the target is tried to treat the classification object on line and carry out classification operation in addition, obtain classification operation data, then can carry out the analysis to classification operation data, obtain the classification result, thereby can solve prior art's condition restriction problem, can also avoid the interference of influence factor, make categorised combination user characteristic obtain the optimal result, time saving and high efficiency, adopt in addition to be tried out the technological means of target through online selection, the online management to user information has been realized.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of an inline classification method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a main flow of generating objects to be classified according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a main flow of analyzing sort operation data according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a main flow of an online typing method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the main modules of an online classification device according to an embodiment of the invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
At present, a traditional card classification method is adopted to classify function modules and menus of internet products, for example, each function name (general number is more than 20) contained in an APP (namely, a third-party application program of mobile equipment) of a mobile phone bank is written on a card, a function card is required to be classified into 3 to 5 major classes, then a researcher simply records the classification behavior of the tested card, interviews are carried out after classification is completed, final classification data is analyzed, and the primary navigation function class of the APP is obtained. The tested object is a user of a research object, specifically a person, that is, the current classification mainly includes that the person manually classifies information cards, interview communication is needed after classification, and theoretically, remote video or telephone operation is available, but the tested object needs to be conducted by an agent, so that the problem of long time period exists.
However, the traditional card classification method has more condition settings, such as conditions of fields, time and the like, and the process is complicated; the manual participation of researchers causes more influence factors and generates data interference, such as whether the description of the card content is standard, whether writing or printing is clear, the size, the color and the font of the card are consistent, whether the stacking sequence of the card before classification is random, whether the card is consistent with the communication technique to be tested, whether the classification process is interfered, whether the interview content is consistent, whether the record analysis process is standard and the like; in addition, the final classification result is subjected to data analysis at present, so that the needs of the tested object are mined and the psychological model is not deeply understood, and the accuracy of the final result is reduced.
In order to solve the above problems, embodiments of the present invention provide an online classification method, which can change the defects and shortcomings of the conventional classification method, implement online classification, avoid limitations of conditions such as field and time, and implement online management on user information, so that classification is combined with user features to obtain an optimal result, and is time-saving and efficient. Fig. 1 is a schematic diagram of main steps of an online serialization classification method according to an embodiment of the present invention, as shown in fig. 1, the main steps of the online serialization classification method may include steps S101 to S103.
Step S101, receiving a product classification request, and generating an object to be classified corresponding to a target product according to the product classification request.
In the online classification method provided by the embodiment of the invention, the product classification request can be received online, and then the object to be classified corresponding to the target product can be generated according to the received product classification request. The target product refers to an internet product requiring functional module division, for example, when designing an APP, the functional classification of the APP needs to be planned, and at this time, the APP can be regarded as a target product; the object to be classified may be a function module or a menu of the target product, or may be another object that needs to be classified, for example, if the classification request is to classify the function of an APP, the object to be classified may be the function of the APP. It should be noted that the number of the objects to be classified is at least one.
In the classification method in the prior art, paper cards need to be manufactured, so that a plurality of influence factors are generated, and data interference is generated, for example, whether the description of the card content is standard, whether writing or printing is clear, whether the card size, the color and the font are consistent, and the like, and the influence factors can generate the data interference. In the embodiment of the invention, the object to be classified can be directly generated according to the on-line received classification request, so that the problem of data interference caused by the traditional card is solved.
And S102, setting a to-be-tested recruitment standard corresponding to the target product, and selecting a target to-be-tested corresponding to the target product according to the to-be-tested recruitment standard.
In the online classification method, the object to be classified needs to be classified, so the target object corresponding to the target product is selected in step S102. The target object is a user who classifies the object to be classified corresponding to the target product. In the process of selecting the target subject, the subject recruitment criteria corresponding to the target product, that is, the subject recruitment conditions, such as demographic characteristics, life experience, behavior habits, and the like, may be set first. Then, target subjects corresponding to the target products may be selected according to the set subject recruitment criterion, for example, if the subject recruitment criterion is college students, the selected target subjects need to be university scholars, and if the subject recruitment criterion is a user who has performed classification operation more than 3 times, the selected target subjects need to have classification experience more than 3 times. In the embodiment of the invention, the online management of the user information is realized by selecting the target to be tested online.
Step S103, obtaining the classification operation data of the object to be classified of the target, analyzing the classification operation data, and obtaining a classification result.
In the prior art, a card needs to be invited to a spacious and private place, the card is tried to be classified in relatively fixed time, conditions such as the place and the time are limited, the process is complicated, and influence factors such as whether the stacking sequence of the cards before classification is random or not, whether the classification process is interfered or not and the like exist. In the embodiment of the invention, the object to be classified is generated through the step S101, the target object is selected through the step S102, in the step S103, the target object can be classified and operated on line to obtain the classification operation data, and then the classification operation data can be analyzed to obtain the classification result, so that the problem of condition limitation in the prior art can be solved, the interference of influencing factors can be avoided, the optimal result can be obtained by classifying and combining with the user characteristics, and the time and the efficiency are saved.
In the online classification method, objects to be classified need to be classified, so how to generate the objects to be classified is an important component of the scheme. As a reference embodiment of the present invention, the step S101 of generating an object to be classified corresponding to a target product according to a product classification request may include:
step S1011, obtaining attribute information corresponding to the target product according to the product classification request;
step S1012, inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating an object to be classified.
Wherein the attribute information may include at least one of the following options: function information, menu information, spatial element information. The function information refers to functions of a target product, and taking a mobile banking APP as an example, the included functions may include: credit card application, credit card repayment, account balance inquiry, mobile phone number transfer and other functions; the menu information may refer to a list of options appearing on the display screen during the progress of the electronic computer program, or may refer to a list function of various service items; the space element information refers to the content related to the target product when the target product is a space field, and elements such as an ATM (automatic teller machine), a queuing machine, a calling machine, a filling machine, a seat, a guide board and the like need to be classified when a bank outlet is designed.
After the attribute information corresponding to the target product is acquired, an object corresponding to the attribute information can be determined, and then the acquired attribute information is input into the object corresponding to the attribute information in a text form or a picture form, so that an object to be classified can be generated. And inputting the function information, menu information or space element information of the target product into the corresponding object in the form of characters or pictures, so that the explanation of the object can be completed, and the object to be classified is obtained. In addition, in the embodiment of the present invention, after the object to be classified is generated, the tag identifier corresponding to the object to be classified may be determined, and then the target object may be tested to classify by using the tag identifier.
Fig. 2 is a schematic diagram of a main process of generating an object to be classified according to an embodiment of the present invention, and as shown in fig. 2, the main process of generating the object to be classified may include:
step S201, receiving a product classification request;
step S202, obtaining attribute information corresponding to the target product according to the product classification request, where the attribute information may include at least one of the following options: function information, menu information, spatial element information;
and step S203, inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating an object to be classified.
The embodiment of the invention can obtain the attribute information according to the classification request received on line, and then directly generate the object to be classified by using the attribute information, thereby solving the problem of data interference caused by the traditional card.
Analysis of the classification operation data is another important component of the online classification method of embodiments of the present invention. Before the classification operation data is acquired, a classification rule needs to be set, and then classification operation is tried to be performed based on the set classification rule. Therefore, as another reference embodiment of the present invention, after receiving the product classification request, the online classification method may further include: and setting a classification rule corresponding to the target product according to the product classification request.
The classification rule refers to a rule which is to be referred to when the object to be classified is classified. The classification rule may set a classification level or architecture, for example, the classification level includes three levels, and the object to be classified may be divided into one level, two levels, or three levels. The classification rule may or may not include a classification type. Taking a certain mobile banking APP as an example, if a classification category is set in the classification rule, the first-level category may include: credit and savings cards; the secondary categories corresponding to credit cards may include: credit card application, credit card repayment and credit card limit inquiry; the three classes corresponding to the credit card limit inquiry can comprise: used quota inquiry, available quota inquiry and temporary quota inquiry. It can be found that if a classification category is set in the classification rule, the number of categories is limited in the classification rule, and obviously, if a classification category is not set in the classification rule, the number of categories is not limited in the classification rule.
In addition, the product classification request of the embodiment of the present invention may further include unique identification information of the target product, belonging type information of the target product, and operation authority information. The operation authority information refers to authority information for restricting the participant.
In the embodiment of the present invention, after the classification rule is set according to the product classification request, obtaining the classification operation data of the target object to be tested for classification may include: sending the object to be classified and the classification rule to a target object to be tested according to a preset form; and after the target object is tested to operate the object to be classified based on the classification rule, acquiring classification operation data. The preset form can be a two-dimensional code, a bar code or a web page link. For example, a two-dimensional code, a barcode or a web page link carrying an object to be classified and a classification rule is sent to a target subject, so that the target subject performs classification operation on data to be classified to obtain classification operation data when the target subject scans the two-dimensional code or the barcode or opens the web page link and conforms to the classification rule.
Because the classification rule can be set with or without classification category, the target is tested to carry out corresponding classification operation according to different classification rules. In a first case, if the classification rule includes a classification category, the target object being tested to operate on the object to be classified based on the classification rule may be: the target is tried to add the object to be classified to the classification category. In the second case, if the classification rule does not include the classification category, the target object being tested to operate on the object to be classified based on the classification rule may be: the target is tested to define classification categories in a self-defining mode, and then the object to be classified is added into the defined classification categories. The target subject custom classification categories may be named for custom level categories as well as for defined categories. Moreover, the target object can also label the information of the object to be classified which is in doubt.
The acquisition of the classification operation data can be regarded as a human-computer interaction process, and from the perspective of a front-end visual interface, the classification operation data comprises an object region to be classified and a classification region. In the object area to be classified, the objects to be classified can be randomly and sequentially stacked, and one object to be classified is displayed each time; the target object in the classification area can be tested to sequentially pull the objects to be classified in the area to be classified into the classification area, and the objects to be classified can also be subjected to text remark explanation. From the back end system, the object to be classified area needs to randomly show the object to be classified, when the target is tested to perform classification operation, the system marks the object to be classified in the classification directory, the marked object to be classified no longer appears in the object area to be classified, and the mark can be modified when the marked object to be classified moves to other classifications. In addition, before the target is tried to finish confirming and submitting, the object to be classified can be freely moved.
In the embodiment of the invention, in the process of browsing and classifying the target to be tested, the background system records the classification result and the operation behavior data of the target to be tested in the whole process, wherein the classification result and the operation behavior data can comprise filled information, the position, the level, the classification duration, the adjustment path and the like of each object to be classified. Thus, the sorting operation data may include: classification result data and classification process data. As still another reference example of the present invention, the analyzing the classification operation data in step S103 to obtain a classification result may include:
step S1031, analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data;
step S1032, analyzing the classification process data to obtain second analysis data corresponding to the classification process data, where the classification process data includes: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data;
step S1033, modifying the first analysis data by using the second analysis data, and generating a classification framework manner and a classification category corresponding to the object to be classified.
The data analysis in the embodiment of the present invention may include analyzing classification result data and analyzing classification process data. Wherein, analyzing the classification result data may be: and analyzing the classification result data by using a preset cluster analysis algorithm, wherein the preset cluster analysis algorithm can comprise the matrix correlation degree and cluster analysis of the object to be classified. The matrix relevancy of the object to be classified is mainly used for checking the classification result and the class name of the target tendency to be tested, the optimal classification result and the class name can be directly found according to the quantitative result, or a very useful reference can be provided for the classification result and the class name. Regarding the optimal category naming, the naming texts of all the target objects which are tested to all the categories can be extracted for text analysis, wherein the naming with the highest frequency or half of the same naming is determined as the optimal naming; regarding the best classification result, according to the clustering analysis result, the lower the clustering score of different objects is, the higher the probability of classifying into a group is, the higher the association degree is, the clustering score can be generated among the objects to be classified, the number of classifications under different scores is different, and the proper clustering score can be found according to the required classification number, namely the best classification is realized. In addition, there are two main analysis methods for cluster analysis: a fully consistent approach and an optimal merging approach. The completely consistent method pays attention to the difference among the objects to be classified, and the objects are respectively listed as long as the objects are different; the optimal merging method considers consistency and homologous relation among objects to be classified, and partial consistency is classified into one class. In addition, the two technical means of matrix correlation and cluster analysis of the objects to be classified belong to mature technologies in the industry, and detailed description is not provided in the embodiment of the invention.
The analysis of the classification process data may be: and selecting classification dimension data, classification position data, classification duration data, classification adjustment path data, classification adjustment frequency data and the like for analysis. For example, the classification duration data refers to the time from the occurrence of the object region to be classified to the determination of the classification region, and can be used as a basis for the classification difficulty corresponding to the object to be classified; the adjustment frequency data refers to the moving frequency of the object to be classified when the object to be classified is dragged to a certain classification for the first time and is finally submitted, and the more the frequency is, the larger the classification dispute is; adjusting path data refers to the process by which an object to be classified is moved to a different classification.
The classification result data are analyzed mainly by analyzing the classification result of a target object to be classified, only data results can be obtained, and interpretation of the results cannot be realized, and the classification process data are analyzed mainly for researching the possibility of existence of the classification result, wherein if the classification result of a certain object to be classified is between the A large class and the B large class and is closer to the A large class, the times or duration or process of classification of the object to be classified is researched through process analysis, so that whether the target object to be classified has knots or uncertainties or not can be determined. The final output conclusion is based on the data of the result analysis, and the data of the process analysis is used as a comment or a description, so in step S1033, the data of the result analysis is modified by the data of the process analysis to generate a classification architecture mode and classification categories corresponding to the objects to be classified, for example, the objects to be classified are classified into 5 categories, and if the objects to be classified are classified into 5 categories, each category includes some objects to be classified, and in the classification process, the objects to be classified have the situations of long operation time and many times of classification adjustment, and then the results of the large category to which the objects to be classified belong can be further analyzed.
Fig. 3 is a schematic diagram of a main flow of analyzing the classification operation data according to an embodiment of the present invention, and as shown in fig. 3, the main flow of analyzing the classification operation data may include:
step S301, setting a classification rule corresponding to a target product according to a product classification request, and sending an object to be classified and the classification rule to a target object to be tested according to a preset form;
step S302, judging whether the classification rules include classification categories, if so, executing step S303, otherwise, executing step S304;
step S303, adding an object to be classified into a classification class by a target object to be tested to obtain classification result data and classification process data;
step S304, the target is tested to define classification categories by self, and then objects to be classified are added into the defined classification categories to obtain classification result data and classification process data;
step S305, analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data;
step S306, analyzing the classification process data to obtain second analysis data corresponding to the classification process data, where the classification process data may include: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data;
and step S307, modifying the first analysis data by using the second analysis data, and generating a classification framework mode and a classification category corresponding to the object to be classified.
In the embodiment of the invention, the target object can be classified on line to obtain the classification operation data, and then the classification operation data can be analyzed to obtain the classification result, so that the problem of condition limitation in the prior art can be solved, the interference of influencing factors can be avoided, the optimal result can be obtained by combining classification with user characteristics, and the time and the efficiency are saved.
The online classification method can comprise three parts of generation of an object to be classified, selection of a target to be tested and analysis of classification operation data. The classification operation data is data obtained by classifying the object to be classified by the target object to be tested, and the final classification result is obtained by analyzing the data, so that the selection of a proper target object to be tested is very important. As still another reference embodiment of the present invention, the step S102 sets a candidate recruitment criterion corresponding to the target product, and selects the target candidate corresponding to the target product according to the candidate recruitment criterion, which may include:
step S1021, setting a subject recruitment standard corresponding to the target product according to the product classification request, wherein the set subject recruitment standard is equivalent to formulating a subject recruitment requirement, such as demographic characteristics, life experience, behavior habits and the like;
step S1022, generating a to-be-recruited questionnaire according to the to-be-recruited standard, and sending the to-be-recruited questionnaire to the user in a preset form, where the to-be-recruited questionnaire may include to-be-recruited questions and options;
step S1023, feedback data of the user to the questionnaire to be recruited is obtained;
and step S1024, selecting a target subject from the users according to the feedback data and the subject recruitment standard.
Specifically, the questionnaires to be recruited containing the recruitment questions are sent to the platform user library or the external user group in the form of two-dimensional codes, bar codes or web page links. Then, whether the recruitment conditions are met or not is judged according to the questionnaires answered and submitted by the users, and then the target subject is selected from the users meeting the recruitment conditions. In addition, in the embodiment of the invention, the target subject is selected on line, so that the online management of the user information can be realized.
Fig. 4 is a schematic diagram of a main flow of an online classification method according to an embodiment of the present invention. As shown in fig. 4, the main flow of the online classification method may include:
step S401, receiving a product classification request;
step S402, obtaining attribute information corresponding to the target product according to the product classification request, where the attribute information may include at least one of the following options: function information, menu information, spatial element information;
step S403, inputting the attribute information into an object corresponding to the attribute information in a character form or a picture form, and generating an object to be classified;
step S404, setting a tested recruitment standard corresponding to the target product according to the product classification request;
step S405, generating a to-be-recruited questionnaire according to a to-be-recruited standard, and sending the to-be-recruited questionnaire to a user according to a preset form;
step S406, obtaining feedback data of the user on the questionnaire to be recruited;
step S407, selecting a target subject from the users according to the feedback data and the subject recruitment standard;
step S408, setting a classification rule corresponding to the target product according to the product classification request, and sending the object to be classified and the classification rule to the target object to be tested according to a preset form;
step S409, judging whether the classification rules include classification categories, if so, executing step S410, otherwise, executing step S411;
step S410, a target object to be classified is added into a classification category to obtain classification result data and classification process data;
step S411, the target is tested to define classification categories by self, and then the object to be classified is added into the defined classification categories to obtain classification result data and classification process data;
step S412, analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data;
step S413, analyzing the classification process data to obtain second analysis data corresponding to the classification process data, where the classification process data may include: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data;
and S414, modifying the first analysis data by utilizing the second analysis data to generate a classification framework mode and a classification category corresponding to the object to be classified.
According to the technical scheme of online classification, the product classification request can be received online, the object to be classified is generated directly according to the classification request, the problem of data interference caused by a traditional card is solved, the target object to be classified can be classified and operated online to obtain the classification operation data, then the classification operation data can be analyzed to obtain the classification result, the problem of condition limitation in the prior art can be solved, interference of influencing factors can be avoided, the classification is combined with user characteristics to obtain the optimal result, time and efficiency are saved, and online management of user information is realized by adopting the technical means of selecting the target object online.
Fig. 5 is a schematic diagram of the main modules of an online classification device according to an embodiment of the present invention. As shown in fig. 5, the main modules of the online classification apparatus 500 may include: a generation module 501, a selection module 502 and an analysis module 503.
The generating module 501 may be configured to receive a product classification request, and generate an object to be classified corresponding to a target product according to the product classification request; the selecting module 502 may be configured to set a candidate recruitment criterion corresponding to the target product, and select a target candidate corresponding to the target product according to the candidate recruitment criterion; the analysis module 503 may be configured to obtain the classification operation data of the object to be classified, and analyze the classification operation data to obtain a classification result.
In this embodiment of the present invention, the generating module 501 may further be configured to: obtaining attribute information corresponding to a target product according to the product classification request; and inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating an object to be classified. Wherein the attribute information may include at least one of the following options: function information, menu information, spatial element information.
In this embodiment of the present invention, the generating module 501 may further be configured to: and setting a classification rule corresponding to the target product according to the product classification request.
In this embodiment of the present invention, the analysis module 503 may further be configured to: sending the object to be classified and the classification rule to a target object to be tested according to a preset form; and after the target object is tested to operate the object to be classified based on the classification rule, acquiring classification operation data.
In the embodiment of the present invention, the classification rule may include a classification category. The target subject attempting to operate on the object to be classified based on the classification rule may include: the target is tried to add the object to be classified to the classification category.
In the embodiment of the invention, the classification rule does not include classification categories. The target subject attempting to operate on the object to be classified based on the classification rule may include: the target is tested to define classification categories in a self-defining mode, and then the object to be classified is added into the defined classification categories.
In this embodiment of the present invention, the classifying operation data may include: classification result data and classification process data. The analysis module 503 may also be configured to: analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data; analyzing the classified process data to obtain second analysis data corresponding to the classified process data, wherein the classified process data comprise: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data; and correcting the first analysis data by using the second analysis data to generate a classification framework mode and a classification category corresponding to the object to be classified.
In this embodiment of the present invention, the selecting module 502 may further be configured to: setting a tested recruitment standard corresponding to a target product according to the product classification request; generating a to-be-tested recruitment questionnaire according to-be-tested recruitment standards, and sending the to-be-tested recruitment questionnaire to a user in a preset form; acquiring feedback data of a user on a questionnaire to be recruited; and selecting a target subject from the users according to the feedback data and the subject recruitment standard.
In an embodiment of the present invention, the preset form may include at least one of the following options: two-dimensional code, bar code, web page link.
From the above description, it can be seen that the online classification device according to the embodiment of the present invention can receive a product classification request online, generate an object to be classified directly according to the classification request, and solve the problem of data interference caused by a conventional card, and a target subject can perform classification operation on the object to be classified online to obtain classification operation data, and then analyze the classification operation data to obtain a classification result, thereby solving the problem of condition limitation in the prior art, avoiding interference of influencing factors, and obtaining an optimal result by classifying in combination with user characteristics, which is time-saving and efficient.
Fig. 6 illustrates an exemplary system architecture 600 of an online classification method or an online classification apparatus to which embodiments of the invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The terminal devices 601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 601, 602, 603. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the online classification method provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the online classification apparatus is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
To the I/O interface 705, AN input section 706 including a keyboard, a mouse, and the like, AN output section 707 including a keyboard such as a Cathode Ray Tube (CRT), a liquid crystal display (L CD), and the like, a speaker, and the like, a storage section 708 including a hard disk and the like, and a communication section 709 including a network interface card such as a L AN card, a modem, and the like, the communication section 709 performs communication processing via a network such as the internet, a drive 710 is also connected to the I/O interface 705 as necessary, a removable medium 711 such as a magnetic disk, AN optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a generation module, a selection module, and an analysis module. The names of the modules do not form a limitation on the modules themselves under certain conditions, for example, the generation module may also be described as a module that receives a product classification request and generates an object to be classified corresponding to a target product according to the product classification request.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: receiving a product classification request, and generating an object to be classified corresponding to a target product according to the product classification request; setting a tested recruitment standard corresponding to a target product, and selecting a target tested corresponding to the target product according to the tested recruitment standard; and obtaining the classification operation data of the object to be classified of the target, analyzing the classification operation data and obtaining a classification result.
According to the technical scheme of the embodiment of the invention, the product classification request can be received online, the object to be classified is directly generated according to the classification request, the problem of data interference caused by the traditional card is solved, the target object to be classified can be classified and operated online to obtain the classification operation data, and then the classification operation data can be analyzed to obtain the classification result, so that the problem of condition limitation in the prior art can be solved, the interference of influencing factors can be avoided, the classification can be combined with the user characteristics to obtain the optimal result, time is saved, efficiency is high, and in addition, the online management of the user information is realized by adopting the technical means of selecting the target object online.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of online classification, comprising:
receiving a product classification request, and generating an object to be classified corresponding to a target product according to the product classification request;
setting a subject recruitment standard corresponding to the target product, and selecting a target subject corresponding to the target product according to the subject recruitment standard;
and obtaining the classification operation data of the target object to be classified, analyzing the classification operation data and obtaining a classification result.
2. The method according to claim 1, wherein the generating an object to be classified corresponding to a target product according to the product classification request comprises:
according to the product classification request, obtaining attribute information corresponding to the target product, wherein the attribute information comprises at least one of the following options: function information, menu information, spatial element information;
and inputting the attribute information into an object corresponding to the attribute information in a text form or a picture form, and generating the object to be classified.
3. The method of claim 1, wherein after receiving the product categorization request, the method further comprises:
and setting a classification rule corresponding to the target product according to the product classification request.
4. The method according to claim 3, wherein the obtaining of the classification operation data of the target object to be classified comprises:
sending the object to be classified and the classification rule to the target object to be tested according to a preset form;
and acquiring the classification operation data after the target object is tried to operate the object to be classified based on the classification rule.
5. The method of claim 4, wherein the classification rules include classification categories; and
the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: the target is tried to add the object to be classified to the classification category.
6. The method of claim 4, wherein the classification rule does not include a classification category; and
the target object is tried to operate the object to be classified based on the classification rule, and the operation comprises the following steps: and the target is tested to define the classification category by self, and then the object to be classified is added into the defined classification category.
7. The method of claim 1, wherein the classification operation data comprises: classification result data and classification process data; and
the analyzing the classification operation data to obtain a classification result includes:
analyzing the classification result data based on a preset cluster analysis algorithm to obtain first analysis data corresponding to the classification result data;
analyzing the classified process data to obtain second analysis data corresponding to the classified process data, wherein the classified process data comprise: the data processing method comprises the following steps of classifying dimension data, classifying position data, classifying duration data, classifying adjusting path data and classifying adjusting frequency data;
and correcting the first analysis data by using the second analysis data to generate a classification framework mode and a classification category corresponding to the object to be classified.
8. The method according to claim 1, wherein the setting of the recruitment criterion corresponding to the target product and the selecting of the target subject corresponding to the target product according to the recruitment criterion comprise:
setting a tested recruitment standard corresponding to the target product according to the product classification request;
generating a to-be-recruited questionnaire according to the to-be-recruited standard, and sending the to-be-recruited questionnaire to a user in a preset form;
acquiring feedback data of the user on the interviewed recruited questionnaire;
selecting the target subject from the user according to the feedback data and the subject recruitment criteria.
9. The method according to claim 4 or claim 8, wherein the preset form comprises at least one of the following options: two-dimensional code, bar code, web page link.
10. A linearized classification apparatus, comprising:
the generation module is used for receiving a product classification request and generating an object to be classified corresponding to a target product according to the product classification request;
the selection module is used for setting a to-be-tested recruitment standard corresponding to the target product and selecting a target to-be-tested corresponding to the target product according to the to-be-tested recruitment standard;
and the analysis module is used for acquiring the classification operation data of the target to be tested on the object to be classified, analyzing the classification operation data and obtaining a classification result.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010187129.3A CN111428159A (en) | 2020-03-17 | 2020-03-17 | Online classification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010187129.3A CN111428159A (en) | 2020-03-17 | 2020-03-17 | Online classification method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111428159A true CN111428159A (en) | 2020-07-17 |
Family
ID=71547934
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010187129.3A Pending CN111428159A (en) | 2020-03-17 | 2020-03-17 | Online classification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111428159A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232731A (en) * | 2020-09-15 | 2021-01-15 | 山东浪潮通软信息科技有限公司 | Method and device for classifying ABC (active bank control) of inventory |
| CN113051444A (en) * | 2021-04-30 | 2021-06-29 | 中国银行股份有限公司 | Method and device for automatically distributing content classification categories |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130179222A1 (en) * | 2011-12-26 | 2013-07-11 | Luth Research, Llc | On-line behavior research method using client/customer survey/respondent groups |
| CN106407234A (en) * | 2015-08-03 | 2017-02-15 | 阿里巴巴集团控股有限公司 | A method and an apparatus for classifying object information in pages |
| CN107093122A (en) * | 2016-12-02 | 2017-08-25 | 北京小度信息科技有限公司 | Object classification method and device |
| CN109086567A (en) * | 2017-07-21 | 2018-12-25 | 上海桑格信息技术有限公司 | The interactive analysis system and method for having the transcriptome project with reference to genome based on cloud computing platform |
| CN109102329A (en) * | 2018-07-27 | 2018-12-28 | 索信市场咨询(北京)有限公司 | A kind of data sampling and processing and analysis application method and device |
| CN109346137A (en) * | 2018-10-29 | 2019-02-15 | 长沙通诺信息科技有限责任公司 | The online recruitment method of subject and device, computer equipment and storage medium |
| CN109359798A (en) * | 2018-08-21 | 2019-02-19 | 平安科技（深圳）有限公司 | Method for allocating tasks, device and storage medium |
| CN109359194A (en) * | 2018-09-30 | 2019-02-19 | 北京字节跳动网络技术有限公司 | Method and apparatus for predictive information classification |
| CN110263329A (en) * | 2019-05-22 | 2019-09-20 | 深圳壹账通智能科技有限公司 | Software product assessment processing method, device, computer equipment and storage medium |
-
2020
- 2020-03-17 CN CN202010187129.3A patent/CN111428159A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130179222A1 (en) * | 2011-12-26 | 2013-07-11 | Luth Research, Llc | On-line behavior research method using client/customer survey/respondent groups |
| CN106407234A (en) * | 2015-08-03 | 2017-02-15 | 阿里巴巴集团控股有限公司 | A method and an apparatus for classifying object information in pages |
| CN107093122A (en) * | 2016-12-02 | 2017-08-25 | 北京小度信息科技有限公司 | Object classification method and device |
| CN109086567A (en) * | 2017-07-21 | 2018-12-25 | 上海桑格信息技术有限公司 | The interactive analysis system and method for having the transcriptome project with reference to genome based on cloud computing platform |
| CN109102329A (en) * | 2018-07-27 | 2018-12-28 | 索信市场咨询(北京)有限公司 | A kind of data sampling and processing and analysis application method and device |
| CN109359798A (en) * | 2018-08-21 | 2019-02-19 | 平安科技（深圳）有限公司 | Method for allocating tasks, device and storage medium |
| CN109359194A (en) * | 2018-09-30 | 2019-02-19 | 北京字节跳动网络技术有限公司 | Method and apparatus for predictive information classification |
| CN109346137A (en) * | 2018-10-29 | 2019-02-15 | 长沙通诺信息科技有限责任公司 | The online recruitment method of subject and device, computer equipment and storage medium |
| CN110263329A (en) * | 2019-05-22 | 2019-09-20 | 深圳壹账通智能科技有限公司 | Software product assessment processing method, device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 甩手小编: "淘宝店铺宝贝分类教程介绍", pages 1 - 9, Retrieved from the Internet <URL:https://wap.shuaishou.com/school/infos10021.html> * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232731A (en) * | 2020-09-15 | 2021-01-15 | 山东浪潮通软信息科技有限公司 | Method and device for classifying ABC (active bank control) of inventory |
| CN112232731B (en) * | 2020-09-15 | 2022-11-22 | 浪潮通用软件有限公司 | Method and equipment for classifying inventory ABC |
| CN113051444A (en) * | 2021-04-30 | 2021-06-29 | 中国银行股份有限公司 | Method and device for automatically distributing content classification categories |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9292877B2 (en) | Methods and systems for generating concept-based hash tags | |
| US11556572B2 (en) | Systems and methods for coverage analysis of textual queries | |
| WO2015055067A1 (en) | Method and apparatus for pushing messages | |
| CN108280200B (en) | Method and device for pushing information | |
| CN112241327A (en) | Shared information processing method and device, storage medium and electronic equipment | |
| US10803390B1 (en) | Method for the management of artifacts in knowledge ecosystems | |
| EP3961426A2 (en) | Method and apparatus for recommending document, electronic device and medium | |
| US20210263916A1 (en) | Systems and methods for automated pattern matching | |
| US20200050906A1 (en) | Dynamic contextual data capture | |
| CN109284367B (en) | Method and device for processing text | |
| CN109933217A (en) | Method and apparatus for pushing sentence | |
| CN111428159A (en) | Online classification method and device | |
| CN107908662A (en) | The implementation method and realization device of search system | |
| CN105260459B (en) | Searching method and device | |
| CN111611514B (en) | Page display method and device based on user login information and electronic equipment | |
| CN113435859A (en) | Letter processing method and device, electronic equipment and computer readable medium | |
| CN110519155B (en) | Information processing method and system | |
| CN110909768A (en) | Method and device for acquiring marked data | |
| US20150220941A1 (en) | Visual tagging to record interactions | |
| CN114297476A (en) | Questionnaire survey method, system, electronic equipment and storage medium based on user tags | |
| CN109493186A (en) | The method and apparatus for determining pushed information | |
| CN113760521A (en) | Virtual resource allocation method and device | |
| CN111581937A (en) | Document generation method and device, computer readable medium and electronic equipment | |
| CN107920100B (en) | Information pushing method and device | |
| CN114170451A (en) | Text recognition method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220923 Address after: 12 / F, 15 / F, 99 Yincheng Road, China (Shanghai) pilot Free Trade Zone, Pudong New Area, Shanghai, 200120 Applicant after: Jianxin Financial Science and Technology Co.,Ltd. Address before: 25 Financial Street, Xicheng District, Beijing 100033 Applicant before: CHINA CONSTRUCTION BANK Corp. Applicant before: Jianxin Financial Science and Technology Co.,Ltd. |