CN111552870A - Object recommendation method, electronic device and storage medium - Google Patents
Object recommendation method, electronic device and storage medium Download PDFInfo
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Abstract
The invention relates to data processing and discloses an object recommendation method, which comprises the following steps: after receiving a technical requirement sent by a first user through a client, analyzing requirement description information to determine a corresponding keyword, and determining a corresponding second label and a corresponding third label according to the keyword; acquiring and analyzing a knowledge map of a preset second user, and primarily screening the preset second user according to an analysis result to determine an initial object; calculating the matching degree of the initial object and the technical requirement, and screening the initial object according to the matching degree to determine a target object; and feeding back the target object to the first user according to the sequence of the matching degree. The invention also discloses an electronic device and a computer storage medium. By using the method and the system, the technical requirements and the technical services can be accurately matched, so that the rapid and accurate recommendation of the object is realized.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an object recommendation method, an electronic device, and a computer-readable storage medium.
Background
With the development of internet technology, technology research and development requirements are more and more, some technical companies can meet the technology research and development requirements by themselves, and other non-technical companies need to provide technical support through technical service providers to meet the technology research and development requirements. Meanwhile, in order to reduce the technical development expenditure, some companies also choose to seek technical support from technical service providers.
At present, on a general research and development demand comprehensive open platform, 2 modes are distributed for demands: 1. the requirements are published on the platform after being confirmed to be real and effective by platform operators, and all technical service providers can see the requirements. This approach may result in a large number of invalid bidding solutions for the requesting party, increasing the lease cost, and the technical service provider may need to pay screening attention among many requests. 2. The demand is distributed by platform operation personnel, and artifical distribution needs the operation personnel to possess the ability of discriminating, and needs to set up corresponding operation flow, consumes the operation human cost. In summary, since the information of both parties is not equal, the technical demander and the technical facilitator cannot be matched quickly and accurately, so that the docking efficiency is seriously reduced.
Most of the existing service providers and requirements are matched by using models for prediction matching, however, model training requires a large amount of data, and is time-consuming and low in accuracy, so that a method for accurately matching technical requirements and technical services to quickly recommend technical service providers to technical demand parties is urgently needed.
Disclosure of Invention
In view of the above, the present invention provides an object recommendation method, an electronic device and a computer-readable storage medium, and a main objective of the invention is to precisely match technical requirements with technical services to realize rapid and precise recommendation of an object.
In order to achieve the above object, the present invention provides an object recommendation method, including:
a receiving step: receiving a technical requirement sent by a first user through a client, wherein the technical requirement comprises a first label and requirement description information corresponding to the technical requirement;
a label determining step: analyzing the requirement description information, determining a keyword corresponding to the requirement description information, determining a second label and a third label corresponding to the technical requirement according to the keyword, and generating a label set of the technical requirement;
a first screening step: acquiring a knowledge map of a preset second user from a preset storage path, analyzing the knowledge map of the preset second user to obtain an analysis result, and screening out a second user matched with the first label of the technical requirement from the preset second user as an initial object according to the analysis result;
a second screening step: calculating the matching degree of the initial object and the technical requirement based on a preset matching degree calculation rule, the analysis result of the knowledge map of the initial object and a second label and a third label corresponding to the technical requirement, and screening out the initial object with the matching degree greater than or equal to a preset threshold value as a target object; and
a recommendation step: and feeding the target object back to the client of the first user according to the sequence of the matching degree.
In addition, the present invention also provides an electronic device, comprising: the object recommendation method comprises a memory and a processor, wherein the memory stores an object recommendation program which can run on the processor, and when the object recommendation program is executed by the processor, any step of the object recommendation method can be realized.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium, which includes an object recommendation program, and when the object recommendation program is executed by a processor, the object recommendation program can implement any of the steps in the object recommendation method as described above.
According to the object recommendation method, the electronic device and the computer-readable storage medium, after technical requirements sent by a first user through a client are received, the requirement description information is analyzed to determine corresponding keywords, and corresponding second tags and third tags are determined according to the keywords; acquiring and analyzing a knowledge map of a preset second user, and primarily screening the preset second user according to an analysis result to determine an initial object; calculating the matching degree of the initial object and the technical requirement, and screening the initial object according to the matching degree to determine a target object; and feeding back the target object to the first user according to the sequence of the matching degree. 1. The method comprises the steps that all levels of node information are determined in advance according to historical business information of each technical service provider (a second user) and a knowledge map is generated, the knowledge map of each technical service provider can be directly obtained to carry out subsequent matching operation in the object recommendation process, and the method is equivalent to carrying out feature processing on information data of each service provider in advance, so that matching and recommendation efficiency is improved; 2. after receiving the technical requirement carrying the requirement description information, analyzing the requirement description information and matching keywords to obtain a complete technical label related to the research and development requirement: the first-level label, the second-level label and the third-level label are used for subsequent screening, calculation and matching recommendation, and based on the technical characteristics, the requirements of the user can be automatically and comprehensively acquired, so that the condition that a technical service provider who performs subsequent recommendation cannot meet the technical requirements of the user is avoided, the matching accuracy is improved, and the user experience is improved; 3. after determining the node information of each level corresponding to each technical facilitator, preliminarily screening the technical facilitators according to the node information of the first level and the first level label of the technical requirement, filtering out the facilitators which do not meet the conditions, reducing subsequent calculation amount, saving calculation resources and improving matching efficiency; 4. the number of required nodes and related nodes of each level of the technical facilitator are determined according to the tags of each level corresponding to the technical requirements, the matching degree of each technical facilitator is calculated by analyzing the implementation case information of the related nodes of the second level, the related nodes of the third level and the related nodes of the fourth level of the technical facilitator, and the objectivity and accuracy of the calculation of the matching degree are improved. In conclusion, model recommendation is not adopted, so that on one hand, training of the model is not required to be carried out by acquiring a large amount of data, the training time is saved, the matching efficiency is improved, on the other hand, the problem that the accuracy of model training is not high does not exist, the matching accuracy is improved, and the user experience is improved; compared with the prior art, the method has the advantages that the considered dimensionality is more comprehensive, the quality, the quantity and the like of each implementation case are considered, the comprehensiveness and the accuracy of the matching degree calculation are improved, the technical research and development requirements of a demand side can be met better, and the satisfaction degree of a technical demand side is improved.
Drawings
FIG. 1 is a flowchart of a preferred embodiment of an object recommendation method of the present invention;
FIG. 2 is a schematic diagram of a knowledge map;
FIG. 3 is a diagram of an electronic device according to a preferred embodiment of the present invention;
fig. 4 is a schematic diagram of program modules of the object recommendation program in fig. 2.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and explanatory of the invention and are not restrictive thereof.
The invention provides an object recommendation method, an electronic device and a computer-readable storage medium, when a first user inputs a technical requirement, the electronic device can acquire requirement information corresponding to the technical requirement, then acquire a knowledge map of a second user (to-be-matched technical service), match the requirement information with the knowledge map, determine that the matched second user is recommended to the first user, can better meet the technical requirement in the actual business of the first user, and avoid a complicated process that the first user searches for the corresponding technical service by himself, so that the efficiency of the first user for selecting the technical service is improved, and the experience of the first user is further improved.
The following detailed description of embodiments of the invention refers to the accompanying drawings.
Referring to fig. 1, a flowchart of a preferred embodiment of the object recommendation method of the present invention is shown. The method may be performed by an apparatus, which may be implemented by software and/or hardware.
In the present embodiment, the object recommendation method includes steps S1-S5.
Step S1, receiving a technical requirement sent by a first user through a client, where the technical requirement includes a first tag and requirement description information corresponding to the technical requirement.
The first tag is used to determine a type of required service, and the technical requirement may include one or more first tags, where the first tag is selected from a preset first tag set, for example, the preset first tag set includes: loan, payment, operation, marketing, etc.
The first user is a technical demander. The client is a mobile terminal used by a first user, and a client APP is installed on the client.
The requirement description information is used for describing the sent technical requirement, so that the service system can analyze the technical requirement and determine a detailed technical label of the technical requirement.
The technical demander selects a corresponding first label from a preset first label set according to the self requirement and inputs detailed requirement description information according to the self requirement. And the technical demand party sends technical demands carrying the first labels and the demand description information to the service system through the client APP of the client.
Step S2, analyzing the requirement description information, determining a keyword corresponding to the requirement description information, determining a second label and a third label corresponding to the technical requirement according to the keyword, and generating a label set of the technical requirement.
The first label is a first-level label, the second label is a second-level label, and the third label is a third-level label. The relationship among the first label, the second label and the third label is as follows: one or more second tags may be included under the first tag, and one or more third tags may be included under the second tag.
The second tag is a specific technical requirement proposed by a technical demander, taking the first tag as a loan as an example, the identified second tag is a credit investigation and wind control engine, and the third tag is a product corresponding to the specific technical requirement proposed by the technical demander, for example, the identified third tag is a score card and the like.
In this embodiment, the analyzing the requirement description information and determining the keyword corresponding to the requirement description information includes:
performing word segmentation on the requirement description information to obtain a word set corresponding to the requirement description information; and
and matching each word in the word set with a preset keyword set, and taking the word matched with the keyword in the preset keyword set as the keyword corresponding to the requirement description information.
The preset keyword set comprises a large number of keywords in the industry. For example, the requirement description information in the technical requirement a is P, after performing word segmentation, word removal, tone word, duplication removal and the like on the requirement description information P, a word set { a1, a2, A3, a4, a5} is obtained, the preset keyword set corresponding to the second tag is { a2, A3, B1, B2, …, BN }, and it is determined that the words that can be matched with the keyword set include { a2, A3}, then the words a2 and A3 are used as keywords corresponding to the requirement description information P, that is, the keywords corresponding to the technical service request a.
It should be noted that the label for parsing and determining the requirement description information is not limited to the second label and the third label, and may be the first label.
In order to improve the accuracy and the integrity of the keyword extraction, the preset keyword library needs to be updated in time, so that the accuracy and the integrity of the subsequent determination of the second label and the third label are improved.
The determining the second label and the third label corresponding to the technical requirement according to the keyword includes:
acquiring mapping data of preset words and labels from a preset storage path, and judging whether words matched with the keywords exist in the mapping data or not; and
and if so, taking the label corresponding to the word matched with the keyword as the label corresponding to the keyword, and taking the label corresponding to the keyword as a second label and a third label corresponding to the technical requirement.
The mapping data of the preset words and the labels comprises: mapping data of the words and the first labels, mapping data of the words and the second labels, and mapping data of the words and the third labels.
It can be understood that, in the actual application process, due to the intersection of services and technologies, the same tag may correspond to one or more keywords (words), and the same keyword (word) may also correspond to one or more tags. For example, in the mapping data of the preset words and the labels: words { W1, W2, W3, … } correspond to first tag W; words { Y1, Y2, Y3, … } correspond to second label Y; words { M1, M2, M3, … } correspond to third tag M; … are provided.
If the keywords extracted in the above steps include Y2 and M3, and the mapping data of the word and the tag is the mapping data corresponding to the second tag, it may be determined that the second tag corresponding to the technical requirement a includes Y, and the corresponding third tag includes M.
Note that not all of the second tags include the third tag. In the process of confirming the tags, the determined third tags and the second tags may not be in a one-to-one correspondence relationship, the identified third tags are obtained, the second tags corresponding to the identified third tags are determined, the determined second tags are added to the analyzed second tags, and if the second tags are confirmed and the third tags corresponding to the second tags are not analyzed, the third tags corresponding to the second tags are determined to be the same as the second tags, for example, the third tags corresponding to the "wind control engine" of the second tags are the "wind control engine".
Step S3, acquiring a knowledge map of a preset second user from a preset storage path, analyzing the knowledge map of the preset second user to obtain an analysis result, and screening out a second user matched with the first label of the technical requirement from the preset second user as an initial object according to the analysis result.
The second user is preset to be all technical service providers in the service system.
In this embodiment, the knowledge map of the second user is preset to be pre-calculated and stored in a preset storage path (e.g., a database). The knowledge map comprises three levels: the primary node (a large category according to the service scope, such as loan, operation and marketing), the secondary node (a large service scope is subdivided, such as loan including credit investigation, incoming articles, wind control engine, collection urging system and the like), and the tertiary node are products corresponding to the secondary node (such as credit investigation including scoring card, credit blacklist and the like).
In this embodiment, the knowledge map of the preset second user is determined by the following steps:
obtaining historical service data of the preset second user within a preset time interval from a preset database, and determining first preset type data, second preset type data, third preset type data and fourth preset type data of the preset second user according to the historical service data;
determining first-level node information according to the first preset type data, determining second-level node information according to the second preset type data, determining third-level node information according to the third preset type data, and determining fourth-level node information according to the fourth preset type data; and
and generating a knowledge map of the preset second user based on the inclusion relationship among the first-level node information, the second-level node information, the third-level node information and the fourth-level node information.
For example, the preset time interval may be the last month, the last quarter, the last year, or the like; the first preset type data is the whole service range of each preset second user, the second preset type data is the detailed service range of each preset second user, the third preset type data is the product information of each preset second user in the service range, and the fourth preset type data is the historical implementation case information corresponding to each product.
The implementation case in the fourth preset type information includes: the number of implementation cases and the detailed information of each implementation case. For example, the detailed information of each implementation case includes the nature, size, etc. of the business of the implementing customer. The determining the fourth-level node information according to the fourth preset type data includes: and grading the implementation cases, wherein the grading comprises the following steps: a star implementation case and a common implementation case, which are comprehensively evaluated according to the enterprise properties (such as a central enterprise, a national enterprise, 500 strong enterprise and the like) and the scale (the number of enterprises) of implementation customers.
For example, by analyzing historical business data of a technical service provider within a preset time interval, the technical service provider provides loan business related services, which include incoming events, wind control engines, collection of credit, and credit investigation, wherein the credit investigation includes: the method comprises the following steps of scoring two products of a card and a credit blacklist, wherein 3 implementation cases exist in the product, 2 implementation cases exist in a wind control engine, and no implementation case exists in collection hastening. The knowledge map of the technical facilitator is shown in figure 2.
In this embodiment, the analyzing the knowledge map of the preset second user to obtain an analysis result, and screening, as an initial object, a second user matched with the first tag of the technical requirement from the preset second users according to the analysis result includes:
analyzing the knowledge map of the preset second user, and respectively determining first-level node information, second-level node information, third-level node information and fourth-level node information of the knowledge map; and
and acquiring primary node information of a knowledge map of the preset second user, acquiring the first label of the technical requirement from the label set of the technical requirement, matching the first label with the primary node information, and screening out the second user successfully matched as an initial object.
In order to save the time of subsequent processing and reduce the calculation amount, technical service providers with service ranges not containing the technical requirements are directly eliminated, and the waste of calculation capacity is avoided.
Step S4, calculating the matching degree of the initial object and the technical requirement based on a preset matching degree calculation rule, the analysis result of the knowledge map of the initial object and the second label and the third label corresponding to the technical requirement, and screening out the initial object with the matching degree greater than or equal to a preset threshold value as a target object.
In this embodiment, the calculating the matching degree between the initial object and the technical requirement based on a preset matching degree calculation rule, an analysis result of the knowledge map of the initial object, and the second tag and the third tag corresponding to the technical requirement includes:
acquiring secondary node information of the knowledge map of the initial object from the analysis result, determining the number of secondary related nodes of the initial object according to the secondary node information and the second label, and judging whether the number of the secondary related nodes of the initial object meets a preset condition or not;
when the number of secondary related nodes of the initial object does not meet a preset condition, judging that the matching degree of the initial object and the technical requirement is 0;
when the number of the secondary related nodes of the initial object meets a preset condition, acquiring tertiary node information of the knowledge map of the initial object from the analysis result, and determining the tertiary related nodes of the initial object according to the third node information and the third label; and
and extracting four-level node information corresponding to the three-level corresponding joint points of the initial object, and calculating the matching degree of the initial object and the technical requirement according to the extracted four-level node information.
The number of the second-level related nodes is the number of the nodes matched with the second label of the first user in the second-level node information of the knowledge map of the initial object.
The third-level related node is a node matched with the third label of the first user in the third-level node information of the knowledge map of the initial object.
For example, the preset condition is that the number of nodes related to the secondary node is greater than or equal to a preset percentage of the number of nodes required by the secondary node, for example, 30%. And the number of the secondary demand nodes is the number of the second labels of the first user. The third-level related node is a third-level node matched with the third label of the first user.
The matching degree is Match Score (MS), the knowledge map node matching degree is Point Score (PS), and the knowledge nodes of different levels are PS2, PS3 and PS 4. The calculation rule of the matching degree is as follows:
if the number of PS2 related nodes is < the number of demand nodes × 30%, MS is 0;
if PS 2-related node number > is 30% of the required node number, the calculation formula of the MS is:
MS＝sum(PS3)＝sum(sum(PS4))
in this embodiment, the calculation formula of PS4 is:
PS4＝α+n*β+m*γ
wherein PS3 represents the matching degree of the three-level correlation node of the initial object, PS4 represents the matching degree of a four-level node under a three-level correlation node of the initial object, α represents the initial matching degree, n represents the number of the star implementation cases under the four-level node, β represents the matching degree of each star implementation case, m represents the number of the common implementation cases under the four-level node, γ represents the matching degree of each common implementation case, and n and m are integers greater than or equal to 0. For example, α is 0.5, β is 1, and γ is 0.2. If the peripheral node has no implementation case, PS4 is 0.5; if the peripheral node is added with one star implementation case PS4 being 0.5+1, and added with one ordinary implementation case PS4 being 0.5+ 0.2.
It should be noted that, the number of implementation cases added to each node is limited (e.g., at most 3), and a star implementation case is preferred.
And step S5, feeding the target object back to the client of the first user according to the sequence of the matching degree.
In order to facilitate the first user to discriminate, the screened technical service providers are sequentially recommended to the technical demanders according to the sequence of the matching degree.
In other embodiments, the feeding back the target object to the client of the first user according to the order of the matching degree includes:
and screening out a preset number of target objects with the matching degrees ranked in the top order and feeding the target objects back to the client of the first user when the number of the target objects exceeds a preset threshold value.
For example, if more than 10 target objects with matching degrees satisfying the requirement are available, only the top 10 (or 8) target objects with matching degrees are selected and fed back to the first user.
In another embodiment, the feeding back the target object to the client of the first user according to the order of the matching degree further includes:
acquiring a blacklist and a white list corresponding to a first user, matching the target object with the blacklist, and deleting the successfully matched target object from the target object; or
When at least two target objects with the same matching degree exist, the at least two target objects with the same matching degree are matched with the white list, and the target objects which are successfully matched are ranked before the target objects which are unsuccessfully matched; or
And when the number of the target objects exceeds a preset threshold value, matching the target objects with the white list, and preferentially screening out the target objects which are close to the matching degree and are successfully matched.
By comparing the blacklist with the white list, the technical service providers which are blacked out by the first user can be excluded before recommendation, the more satisfied technical service providers of the first user can be recommended preferentially, and the use experience of the user is improved.
In other embodiments, the object recommendation method further includes:
and updating and saving the knowledge map of the second user regularly.
Specifically, after a technical service provider completes a demand, an implementation case of the technical service provider is generated based on the demand, and historical business data and a knowledge map of the technical service provider are updated and stored based on the implementation case.
The object recommendation method provided by the embodiment comprises the following steps that 1, node information of each level is determined in advance according to historical service information of each technical service provider (a second user) and a knowledge map is generated, the knowledge map of each technical service provider can be directly obtained to perform subsequent matching operation in the object recommendation process, namely, feature processing is performed on information data of each service provider in advance, and matching and recommendation efficiency is improved; 2. after receiving the technical requirement carrying the requirement description information, analyzing the requirement description information and matching keywords to obtain a complete technical label related to the research and development requirement: the first-level label, the second-level label and the third-level label are used for subsequent screening, calculation and matching recommendation, and based on the technical characteristics, the requirements of the user can be automatically and comprehensively acquired, so that the condition that a technical service provider who performs subsequent recommendation cannot meet the technical requirements of the user is avoided, the matching accuracy is improved, and the user experience is improved; 3. after determining the node information of each level corresponding to each technical facilitator, preliminarily screening the technical facilitators according to the node information of the first level and the first level label of the technical requirement, filtering out the facilitators which do not meet the conditions, reducing subsequent calculation amount, saving calculation resources and improving matching efficiency; 4. the number of required nodes and related nodes of each level of the technical facilitator are determined according to the tags of each level corresponding to the technical requirements, the matching degree of each technical facilitator is calculated by analyzing the implementation case information of the related nodes of the second level, the related nodes of the third level and the related nodes of the fourth level of the technical facilitator, and the objectivity and accuracy of the calculation of the matching degree are improved. In conclusion, model recommendation is not adopted, so that on one hand, training of the model is not required to be carried out by acquiring a large amount of data, the training time is saved, the matching efficiency is improved, on the other hand, the problem that the accuracy of model training is not high does not exist, the matching accuracy is improved, and the user experience is improved; compared with the prior art, the method has the advantages that the considered dimensionality is more comprehensive, the quality, the quantity and the like of each implementation case are considered, the comprehensiveness and the accuracy of the matching degree calculation are improved, the technical research and development requirements of a demand side can be met better, and the satisfaction degree of a technical demand side is improved.
The invention also provides an electronic device. Fig. 3 is a schematic diagram of an electronic device 1 according to a preferred embodiment of the invention.
In this embodiment, the electronic device 1 may be a server, a smart phone, a tablet computer, a portable computer, a desktop computer, or other terminal equipment with a data processing function, where the server may be a rack server, a blade server, a tower server, or a cabinet server.
The electronic device 1 includes a memory 11, a processor 12, and a network interface 13.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic apparatus 1 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic apparatus 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus 1.
The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as the object recommendation program 10, but also to temporarily store data that has been output or will be output.
The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip in some embodiments, and is used for executing program codes or Processing data stored in the memory 11, such as the object recommendation program 10.
The network interface 13 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and is generally used for establishing a communication connection between the electronic apparatus 1 and other electronic devices, such as a client (not shown).
Fig. 3 only shows the electronic device 1 with components 11-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, but may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
Optionally, the electronic device 1 may further comprise a user interface, the user interface may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further comprise a standard wired interface, a wireless interface.
Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display, which may also be referred to as a display screen or display unit, is used for displaying information processed in the electronic apparatus 1 and for displaying a visualized user interface.
In the embodiment of the electronic device 1 shown in fig. 3, the memory 11 as a kind of computer storage medium stores the program code of the object recommendation program 10, and when the processor 12 executes the program code of the object recommendation program 10, the following steps are implemented:
a receiving step: receiving a technical requirement sent by a first user through a client, wherein the technical requirement comprises a first label and requirement description information corresponding to the technical requirement;
the first tag is used to determine a type of required service, and the technical requirement may include one or more first tags, where the first tag is selected from a preset first tag set, for example, the preset first tag set includes: loan, payment, operation, marketing, etc.
The first user is a technical demander. The client is a mobile terminal used by a first user, and a client APP is installed on the client.
The requirement description information is used for describing the sent technical requirement, so that the service system can analyze the technical requirement and determine a detailed technical label of the technical requirement.
The technical demander selects a corresponding first label from a preset first label set according to the self requirement and inputs detailed requirement description information according to the self requirement. And the technical demand party sends technical demands carrying the first labels and the demand description information to the service system through the client APP of the client.
A label determining step: analyzing the requirement description information, determining a keyword corresponding to the requirement description information, determining a second label and a third label corresponding to the technical requirement according to the keyword, and generating a label set of the technical requirement;
the first label is a first-level label, the second label is a second-level label, and the third label is a third-level label. The relationship among the first label, the second label and the third label is as follows: one or more second tags may be included under the first tag, and one or more third tags may be included under the second tag.
The second tag is a specific technical requirement proposed by a technical demander, taking the first tag as a loan as an example, the identified second tag is a credit investigation and wind control engine, and the third tag is a product corresponding to the specific technical requirement proposed by the technical demander, for example, the identified third tag is a score card and the like.
In this embodiment, the analyzing the requirement description information and determining the keyword corresponding to the requirement description information includes:
performing word segmentation on the requirement description information to obtain a word set corresponding to the requirement description information; and
and matching each word in the word set with a preset keyword set, and taking the word matched with the keyword in the preset keyword set as the keyword corresponding to the requirement description information.
The preset keyword set comprises a large number of keywords in the industry. For example, the requirement description information in the technical requirement a is P, after performing word segmentation, word removal, tone word, duplication removal and the like on the requirement description information P, a word set { a1, a2, A3, a4, a5} is obtained, the preset keyword set corresponding to the second tag is { a2, A3, B1, B2, …, BN }, and it is determined that the words that can be matched with the keyword set include { a2, A3}, then the words a2 and A3 are used as keywords corresponding to the requirement description information P, that is, the keywords corresponding to the technical service request a.
It should be noted that the label for parsing and determining the requirement description information is not limited to the second label and the third label, and may be the first label.
In order to improve the accuracy and the integrity of the keyword extraction, the preset keyword library needs to be updated in time, so that the accuracy and the integrity of the subsequent determination of the second label and the third label are improved.
The determining the second label and the third label corresponding to the technical requirement according to the keyword includes:
acquiring mapping data of preset words and labels from a preset storage path, and judging whether words matched with the keywords exist in the mapping data or not; and
and if so, taking the label corresponding to the word matched with the keyword as the label corresponding to the keyword, and taking the label corresponding to the keyword as a second label and a third label corresponding to the technical requirement.
The mapping data of the preset words and the labels comprises: mapping data of the words and the first labels, mapping data of the words and the second labels, and mapping data of the words and the third labels.
It can be understood that, in the actual application process, due to the intersection of services and technologies, the same tag may correspond to one or more keywords (words), and the same keyword (word) may also correspond to one or more tags. For example, in the mapping data of the preset words and the labels: words { W1, W2, W3, … } correspond to first tag W; words { Y1, Y2, Y3, … } correspond to second label Y; words { M1, M2, M3, … } correspond to third tag M; … are provided.
If the keywords extracted in the above steps include Y2 and M3, and the mapping data of the word and the tag is the mapping data corresponding to the second tag, it may be determined that the second tag corresponding to the technical requirement a includes Y, and the corresponding third tag includes M.
Note that not all of the second tags include the third tag. In the process of confirming the tags, the determined third tags and the second tags may not be in a one-to-one correspondence relationship, the identified third tags are obtained, the second tags corresponding to the identified third tags are determined, the determined second tags are added to the analyzed second tags, and if the second tags are confirmed and the third tags corresponding to the second tags are not analyzed, the third tags corresponding to the second tags are determined to be the same as the second tags, for example, the third tags corresponding to the "wind control engine" of the second tags are the "wind control engine".
A first screening step: acquiring a knowledge map of a preset second user from a preset storage path, analyzing the knowledge map of the preset second user to obtain an analysis result, and screening out a second user matched with the first label of the technical requirement from the preset second user as an initial object according to the analysis result;
the second user is preset to be all technical service providers in the service system.
In this embodiment, the knowledge map of the second user is preset to be pre-calculated and stored in a preset storage path (e.g., a database). The knowledge map comprises three levels: the primary node (a large category according to the service scope, such as loan, operation and marketing), the secondary node (a large service scope is subdivided, such as loan including credit investigation, incoming articles, wind control engine, collection urging system and the like), and the tertiary node are products corresponding to the secondary node (such as credit investigation including scoring card, credit blacklist and the like).
In this embodiment, the knowledge map of the preset second user is determined by the following steps:
obtaining historical service data of the preset second user within a preset time interval from a preset database, and determining first preset type data, second preset type data, third preset type data and fourth preset type data of the preset second user according to the historical service data;
determining first-level node information according to the first preset type data, determining second-level node information according to the second preset type data, determining third-level node information according to the third preset type data, and determining fourth-level node information according to the fourth preset type data; and
and generating a knowledge map of the preset second user based on the inclusion relationship among the first-level node information, the second-level node information, the third-level node information and the fourth-level node information.
For example, the preset time interval may be the last month, the last quarter, the last year, or the like; the first preset type data is the whole service range of each preset second user, the second preset type data is the detailed service range of each preset second user, the third preset type data is the product information of each preset second user in the service range, and the fourth preset type data is the historical implementation case information corresponding to each product.
The implementation case in the fourth preset type information includes: the number of implementation cases and the detailed information of each implementation case. For example, the detailed information of each implementation case includes the nature, size, etc. of the business of the implementing customer. The determining the fourth-level node information according to the fourth preset type data includes: and grading the implementation cases, wherein the grading comprises the following steps: a star implementation case and a common implementation case, which are comprehensively evaluated according to the enterprise properties (such as a central enterprise, a national enterprise, 500 strong enterprise and the like) and the scale (the number of enterprises) of implementation customers.
For example, by analyzing historical business data of a technical service provider within a preset time interval, the technical service provider provides loan business related services, which include incoming events, wind control engines, collection of credit, and credit investigation, wherein the credit investigation includes: the method comprises the following steps of scoring two products of a card and a credit blacklist, wherein 3 implementation cases exist in the product, 2 implementation cases exist in a wind control engine, and no implementation case exists in collection hastening. The knowledge map of the technical facilitator is shown in figure 2.
In this embodiment, the analyzing the knowledge map of the preset second user to obtain an analysis result, and screening, as an initial object, a second user matched with the first tag of the technical requirement from the preset second users according to the analysis result includes:
analyzing the knowledge map of the preset second user, and respectively determining first-level node information, second-level node information, third-level node information and fourth-level node information of the knowledge map;
and acquiring primary node information of a knowledge map of the preset second user, acquiring the first label of the technical requirement from the label set of the technical requirement, matching the first label with the primary node information, and screening out the second user successfully matched as an initial object.
In order to save the time of subsequent processing and reduce the calculation amount, technical service providers with service ranges not containing the technical requirements are directly eliminated, and the waste of calculation capacity is avoided.
A second screening step: calculating the matching degree of the initial object and the technical requirement based on a preset matching degree calculation rule, the analysis result of the knowledge map of the initial object and a second label and a third label corresponding to the technical requirement, and screening out the initial object with the matching degree greater than or equal to a preset threshold value as a target object;
in this embodiment, the calculating the matching degree between the initial object and the technical requirement based on a preset matching degree calculation rule, an analysis result of the knowledge map of the initial object, and the second tag and the third tag corresponding to the technical requirement includes:
acquiring secondary node information of the knowledge map of the initial object from the analysis result, determining the number of secondary related nodes of the initial object according to the secondary node information and the second label, and judging whether the number of the secondary related nodes of the initial object meets a preset condition or not;
when the number of secondary related nodes of the initial object does not meet a preset condition, judging that the matching degree of the initial object and the technical requirement is 0;
when the number of the secondary related nodes of the initial object meets a preset condition, acquiring tertiary node information of the knowledge map of the initial object from the analysis result, and determining the tertiary related nodes of the initial object according to the third node information and the third label; and
and extracting four-level node information corresponding to the three-level corresponding joint points of the initial object, and calculating the matching degree of the initial object and the technical requirement according to the extracted four-level node information.
The number of the second-level related nodes is the number of the nodes matched with the second label of the first user in the second-level node information of the knowledge map of the initial object.
The third-level related node is a node matched with the third label of the first user in the third-level node information of the knowledge map of the initial object.
For example, the preset condition is that the number of nodes related to the secondary node is greater than or equal to a preset percentage of the number of nodes required by the secondary node, for example, 30%. And the number of the secondary demand nodes is the number of the second labels of the first user. The third-level related node is a third-level node matched with the third label of the first user.
The matching degree is Match Score (MS), the knowledge map node matching degree is Point Score (PS), and the knowledge nodes of different levels are PS2, PS3 and PS 4. The calculation rule of the matching degree is as follows:
if the number of PS2 related nodes is < the number of demand nodes × 30%, MS is 0;
if PS 2-related node number > is 30% of the required node number, the calculation formula of the MS is:
MS＝sum(PS3)＝sum(sum(PS4))
in this embodiment, the calculation formula of PS4 is:
PS4＝α+n*β+m*γ
wherein PS3 represents the matching degree of the three-level correlation node of the initial object, PS4 represents the matching degree of a four-level node under a three-level correlation node of the initial object, α represents the initial matching degree, n represents the number of the star implementation cases under the four-level node, β represents the matching degree of each star implementation case, m represents the number of the common implementation cases under the four-level node, γ represents the matching degree of each common implementation case, and n and m are integers greater than or equal to 0. For example, α is 0.5, β is 1, and γ is 0.2. If the peripheral node has no implementation case, PS4 is 0.5; if the peripheral node is added with one star implementation case PS4 being 0.5+1, and added with one ordinary implementation case PS4 being 0.5+ 0.2.
It should be noted that, the number of implementation cases added to each node is limited (e.g., at most 3), and a star implementation case is preferred.
A recommendation step: and feeding the target object back to the client of the first user according to the sequence of the matching degree.
In order to facilitate the first user to discriminate, the screened technical service providers are sequentially recommended to the technical demanders according to the sequence of the matching degree.
In other embodiments, the feeding back the target object to the client of the first user according to the order of the matching degree includes:
and screening out a preset number of target objects with the matching degrees ranked in the top order and feeding the target objects back to the client of the first user when the number of the target objects exceeds a preset threshold value.
For example, if more than 10 target objects with matching degrees satisfying the requirement are available, only the top 10 (or 8) target objects with matching degrees are selected and fed back to the first user.
In another embodiment, the feeding back the target object to the client of the first user according to the order of the matching degree further includes:
acquiring a blacklist and a white list corresponding to a first user, matching the target object with the blacklist, and deleting the successfully matched target object from the target object; or
When at least two target objects with the same matching degree exist, the at least two target objects with the same matching degree are matched with the white list, and the target objects which are successfully matched are ranked before the target objects which are unsuccessfully matched; or
And when the number of the target objects exceeds a preset threshold value, matching the target objects with the white list, and preferentially screening out the target objects which are close to the matching degree and are successfully matched.
By comparing the blacklist with the white list, the technical service providers which are blacked out by the first user can be excluded before recommendation, the more satisfied technical service providers of the first user can be recommended preferentially, and the use experience of the user is improved.
In other embodiments, the processor 12, when executing the program code of the object recommendation program 10, further implements the steps of:
and updating and saving the knowledge map of the second user regularly.
Specifically, after a technical service provider completes a demand, an implementation case of the technical service provider is generated based on the demand, and historical business data and a knowledge map of the technical service provider are updated and stored based on the implementation case.
Alternatively, in other embodiments, the object recommendation program 10 can be divided into one or more modules, and the one or more modules are stored in the memory 11 and executed by one or more processors (in this embodiment, the processor 12) to implement the present invention. For example, referring to fig. 4, which is a schematic block diagram of the object recommendation program 10 in fig. 3, in this embodiment, the object recommendation program 10 may be divided into modules 110 and 150, and the functions or operation steps implemented by the modules 110 and 150 are similar to those described above, which are not described in detail here, for example, wherein:
a receiving module 110, configured to receive a technical requirement sent by a first user through a client, where the technical requirement includes a first tag and requirement description information corresponding to the technical requirement;
a tag determination module 120, configured to analyze the requirement description information, determine a keyword corresponding to the requirement description information, determine a second tag and a third tag corresponding to the technical requirement according to the keyword, and generate a tag set of the technical requirement;
the first screening module 130 is configured to acquire a knowledge map of a preset second user from a preset storage path, analyze the knowledge map of the preset second user to obtain an analysis result, and screen out, from the preset second users, a second user matched with the first tag of the technical requirement as an initial object according to the analysis result;
a second screening module 140, configured to calculate a matching degree between the initial object and the technical requirement based on a preset matching degree calculation rule, an analysis result of the knowledge map of the initial object, and a second tag and a third tag corresponding to the technical requirement, and screen out an initial object whose matching degree is greater than or equal to a preset threshold value, as a target object; and
and the recommending module 150 is configured to feed back the target object to the client of the first user according to the sequence of the matching degrees.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes an object recommendation program 10, and when executed by a processor, the object recommendation program 10 implements the steps of the object recommendation method described above.
The embodiment of the computer-readable storage medium of the present invention is substantially the same as the embodiment of the object recommendation method, and will not be described herein again.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An object recommendation method applied to an electronic device is characterized by comprising the following steps:
a receiving step: receiving a technical requirement sent by a first user through a client, wherein the technical requirement comprises a first label and requirement description information corresponding to the technical requirement;
a label determining step: analyzing the requirement description information, determining a keyword corresponding to the requirement description information, determining a second label and a third label corresponding to the technical requirement according to the keyword, and generating a label set of the technical requirement;
a first screening step: acquiring a knowledge map of a preset second user from a preset storage path, analyzing the knowledge map of the preset second user to obtain an analysis result, and screening out a second user matched with the first label of the technical requirement from the preset second user as an initial object according to the analysis result;
a second screening step: calculating the matching degree of the initial object and the technical requirement based on a preset matching degree calculation rule, the analysis result of the knowledge map of the initial object and a second label and a third label corresponding to the technical requirement, and screening out the initial object with the matching degree greater than or equal to a preset threshold value as a target object; and
a recommendation step: and feeding the target object back to the client of the first user according to the sequence of the matching degree.
2. The object recommendation method according to claim 1, wherein the parsing the requirement description information and determining the keyword corresponding to the requirement description information includes:
performing word segmentation on the requirement description information to obtain a word set corresponding to the requirement description information; and
and matching each word in the word set with a preset keyword set, and taking the word matched with the keyword in the preset keyword set as the keyword corresponding to the requirement description information.
3. The object recommendation method according to claim 1, wherein the determining the second tag and the third tag corresponding to the technical requirement according to the keyword comprises:
acquiring mapping data of preset words and labels from a preset storage path, and judging whether words matched with the keywords exist in the mapping data or not; and
and if so, taking the label corresponding to the word matched with the keyword as the label corresponding to the keyword, and taking the label corresponding to the keyword as a second label and a third label corresponding to the technical requirement.
4. The object recommendation method of claim 1, wherein the knowledge map of the preset second user is determined by:
obtaining historical service data of the preset second user within a preset time interval from a preset database, and determining first preset type data, second preset type data, third preset type data and fourth preset type data of the preset second user according to the historical service data;
determining first-level node information according to the first preset type data, determining second-level node information according to the second preset type data, determining third-level node information according to the third preset type data, and determining fourth-level node information according to the fourth preset type data; and
and generating a knowledge map of the preset second user based on the inclusion relationship among the first-level node information, the second-level node information, the third-level node information and the fourth-level node information.
5. The object recommendation method according to any one of claims 1 to 4, wherein the calculating the matching degree between the initial object and the technical requirement based on a preset matching degree calculation rule, an analysis result of the knowledge map of the initial object, and a second tag and a third tag corresponding to the technical requirement comprises:
acquiring secondary node information of the knowledge map of the initial object from the analysis result, determining the number of secondary related nodes of the initial object according to the secondary node information and the second label, and judging whether the number of the secondary related nodes of the initial object meets a preset condition or not;
when the number of secondary related nodes of the initial object does not meet a preset condition, judging that the matching degree of the initial object and the technical requirement is 0;
when the number of the secondary related nodes of the initial object meets a preset condition, acquiring tertiary node information of the knowledge map of the initial object from the analysis result, and determining the tertiary related nodes of the initial object according to the third node information and the third label; and
extracting four-level node information corresponding to three-level corresponding joint points of the initial object, and calculating the matching degree of the initial object and the technical requirement according to the extracted four-level node information, wherein the calculation formula of the matching degree is as follows:
MS＝sum(PS3)＝sum(sum(PS4))
PS4＝α+n*β+m*γ
wherein PS3 represents the matching degree of the three-level correlation node of the initial object, PS4 represents the matching degree of a four-level node under a three-level correlation node of the initial object, α represents the initial matching degree, n represents the number of the star implementation cases under the four-level node, β represents the matching degree of each star implementation case, m represents the number of the common implementation cases under the four-level node, γ represents the matching degree of each common implementation case, and n and m are integers greater than or equal to 0.
6. The object recommendation method according to claim 1, wherein the analyzing the knowledge map of the preset second user to obtain an analysis result, and screening out a second user matched with the first tag of the technical requirement from preset second users as an initial object according to the analysis result, includes:
analyzing the knowledge map of the preset second user, and respectively determining first-level node information, second-level node information, third-level node information and fourth-level node information of the knowledge map; and
and acquiring primary node information of a knowledge map of the preset second user, acquiring the first label of the technical requirement from the label set of the technical requirement, matching the first label with the primary node information, and screening out the second user successfully matched as an initial object.
7. The object recommendation method according to claim 1, wherein the feeding back the target object to the client of the first user according to the matching degree in the ascending and descending order comprises:
and screening out a preset number of target objects with the matching degrees ranked in the top order and feeding the target objects back to the client of the first user when the number of the target objects exceeds a preset threshold value.
8. The object recommendation method according to claim 4, further comprising:
and updating and saving the knowledge map of the second user regularly.
9. An electronic device, comprising: a memory, a processor, the memory storing an object recommendation program operable on the processor, the object recommendation program when executed by the processor implementing the steps of the object recommendation method according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes an object recommendation program, which when executed by a processor can implement the steps of the object recommendation method according to any one of claims 1 to 8.
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