CN111460280A - Legal service personnel recommendation method based on public legal service platform - Google Patents
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Abstract
The embodiment of the disclosure provides a legal service personnel recommendation method and device based on a public legal service platform. The method comprises the following steps: acquiring personal information and application characteristic information of a login user; judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user; calculating a recommendation index of legal service personnel according to the recommendation rule; and recommending legal service personnel to the login user according to the legal service personnel recommendation index. In this way, more intelligent and personalized public legal service personnel recommendation can be realized, and the service quality of the public legal service personnel is improved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of information technology, and more particularly, to a method and apparatus for recommending legal service personnel based on a public legal service platform.
Background
With the development and popularization of internet technology, various public legal service platforms emerge.
Currently, most legal service platforms complete the interface between the public and the practitioners providing legal services in the following two ways: the platform randomly matches social public seeking legal service with public legal service personnel on the platform, which are online and in idle states; the second method comprises the following steps: when the social public initiates a legal service request on the platform, the online public legal service personnel provide legal service in a form of order grabbing.
It can be seen that the match between the public society who presents legal service requirements and the legal practitioner who provides legal service on the existing platform is a mechanical, random approach. Therefore, many problems are exposed, on one hand, random matching or order grabbing matching possibly has the problems that no contra-oral or regional difference exists in legal regulations in the professional field, and legal service personnel cannot provide satisfactory and high-quality legal service for the social public; on the other hand, although the legal service practitioner can select the service requirement of the professional scope when in order grabbing, once the management system mechanism is not perfect and is not matched, the situation that no person grabs the order and no person provides the legal service can occur.
In conclusion, no matter the provided legal service is not high in quality or cannot be provided in time, the public legal service which is equal in popularity, efficient and convenient and can be obtained by people is influenced.
Disclosure of Invention
According to the embodiment of the disclosure, aiming at the problems, a public legal service platform-based legal service personnel recommendation method and device are provided, so that more intelligent and personalized public legal service personnel recommendation can be realized.
In a first aspect of the disclosure, a public legal service platform-based legal service personnel recommendation method is provided. The method comprises the following steps:
acquiring personal information and application characteristic information of a login user;
judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
calculating a recommendation index of legal service personnel according to the recommendation rule;
and recommending legal service personnel to the login user according to the legal service personnel recommendation index.
Further, it is characterized in that,
the personal information includes unique identification information;
the application characteristic information comprises legal consultation and/or transaction information.
Further, the obtaining of the existing recommendation rule includes:
and acquiring the recommendation rule of the user according to the unique identification information of the login user.
Further, it is characterized in that,
the recommendation rule is composed of a recommendation algorithm rule matrix;
the recommendation algorithm rule matrix comprises recommendation parameter information, recommendation participation information and/or recommendation weight information.
Further, the recommended parameter information includes event parameter information and calculation parameter information.
Further, the calculating a recommendation index for legal service personnel according to the recommendation rule comprises:
acquiring online legal service personnel information on a public legal service platform;
screening the legal service personnel information according to the personal information and the application characteristic information of the login user;
and calculating to obtain the recommendation index of the legal service personnel according to the recommendation rule associated with the screened legal service personnel information.
Further, the legal service personnel recommendation index is calculated using the following formula:
wherein R is a recommendation index of legal service personnel;
Pkis a matter parameter;
Lmis PkSpecific information of the item parameters;
UPkrecommending item parameters in the rules for the user;
n is PkThe number of transaction parameters;
Sito calculate the parameters;
Ciis SiCalculating an arithmetic factor of the parameter;
j is SiCalculating the number of parameters;
f is whether to participate in the calculation value, if participate in the calculation, the F is 1; if not, taking 0 as F;
Wkitem parameter UP for a user recommendation rulekA calculated weight in the legal service personnel recommendation;
Wito calculate a parameter SiCalculating a weight in the legal service personnel recommendation.
Further, the recommending legal service personnel to the login user according to the legal service personnel recommendation index comprises:
arranging the screened legal service personnel from large to small according to the recommendation index of the legal service personnel;
and displaying and/or sending information to the user according to the arrangement sequence.
Further, after recommending legal service personnel to the login user according to the legal service personnel recommendation index, the method further comprises the following steps:
analyzing the push information, if the push information is adopted, increasing the calculation according to a preset valueWeight WkAnd/or WiAnd meanwhile, updating the recommendation rule of the user.
In a second aspect of the present disclosure, there is provided a public legal service platform-based legal service personnel recommendation device, including:
the acquisition module is used for acquiring personal information and application characteristic information of a login user;
the judging module is used for judging whether the recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
the calculation module is used for calculating the recommendation index of the legal service personnel according to the recommendation rule;
and the pushing module is used for recommending the legal service personnel to the login user according to the legal service personnel recommendation index.
According to the legal service personnel recommendation method based on the public legal service platform, the information of the login user is analyzed to obtain the corresponding recommendation rule, the legal service personnel recommendation index is calculated according to the recommendation rule, the legal service personnel is recommended to the login user according to the legal service personnel recommendation index, and the service quality of the public legal service personnel is improved; furthermore, the existing public legal service platform is improved, more intelligent and personalized public legal service personnel recommendation is realized, the satisfaction rate of people on public legal service is improved, and the national law enforcement social construction is assisted.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 is a flow chart of a public legal services platform based legal service personnel recommendation method according to the present application;
FIG. 2 is a block diagram of a public legal service platform based legal service personnel recommendation device according to the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
FIG. 1 illustrates a flow chart of a public legal services platform based legal service personnel recommendation method 100 according to the present application. As shown in fig. 1, comprises
And S110, acquiring personal information and application characteristic information of the login user.
And acquiring personal information and application characteristic information of the login user through the mobile application.
The personal information of the login user is acquired so as to generate a recommendation rule according to a user tag, wherein the user tag comprises the gender, the age stage, the region, the education degree, the industry, the code of historical legal service personnel and the like of the user.
When a user registers on the public legal service platform, the user needs to fill in information such as an account number (which needs to be filled in), an identification number (which needs to be filled in), a contact telephone, a standing address, education degree and/or affiliated industries, namely personal information of the user. Meanwhile, the system calculates the age stage of the user according to the identity card number filled by the user. After the registration is successful, the system can store the user personal information such as the user account (user unique identification information) and the like in the server, and further, when the user selects legal service, the system can also store the coding information of the legal service provider in the server. When the user logs in again, the system can acquire the personal information of the logged-in user from the server according to the user account.
Further, the acquired information may be configured by loginn id (unique identification information of the user), gender, age, location, education level, industry to which the user belongs, a historical legal service staff code list, and the like, and further, the historical legal service staff code list is information of legal service staff who have provided legal service for the user in the form of a list, and further, each information field is separated by commas, and each label value in the same information field is separated by semicolons. For example, (loginn id, UP1{ girl }, UP2{ youth }, UP3{ Changsha }, UP4{ Benke }, UP5{ education industry }, UP6{ 23; 58; 99}), which means that the loginn id user is a youth woman, a perennial Changsha, a subject academic, an education industry, and three legal service personnel (coded as 23,58, 99) provide legal services for the user.
The application characteristic information of the user is obtained to further generate a recommendation rule of legal service personnel according to the service items required by the user.
Further, after logging on the public legal service platform, the user may need to seek legal consultation and/or apply for handling public legal service items. Specifically, when a user makes legal consultation, consultation can be made for a certain type of affairs; when a user is transacting a public legal service event, a particular transaction is selected. For example: the user needs to make legal consultation or public certificate inquiry, authentication inquiry and the like on the related problems of intellectual property.
Further, after logging in, the user firstly selects from the service major categories (legal consultation or affair handling), and then selects the service minor category corresponding to the user according to the self requirement. Further, the service subclass of the large class of legal consultations may be marital family consultations, employee dispute consultations, trademark patent consultations, intellectual property consultations, company fiscal and tax consultations, notary consultations, appraisal consultations, and the like. The service subclass of the transaction processing major class can be office certificates, judicial appraisal, law assistance, arbitration, mediation, citizen agent and the like.
Further, the recommendation rule in the application is generated by combining the user application characteristic information and the user personal information. For example, the user selects legal consultation service and further selects to consult the related problems of intellectual property, the obtained user application characteristic information is (loginn id, UP7{ legal consultation }, UP8{ intellectual property }), and after being combined with the personal information of the user, the obtained user application characteristic information is: (loginn ID, UP1{ women }, UP2{ youth }, UP3{ Changsha }, UP4{ Benke }, UP5{ education industry }, UP6{ 23; 58; 99}, UP7{ legal consultancy }, UP8{ intellectual property }).
S120, judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, the existing recommendation rule is obtained, and if the personal information and the application characteristic information do not exist, the recommendation rule of the login user is generated according to the personal information and the application characteristic information of the login user.
And judging whether the recommendation rule associated with the user is stored or generated in the platform or not by the personal information of the login user. If yes, directly calling the existing recommendation rule according to the unique identification information of the login user; if not, generating a new recommendation rule according to the personal information and the application characteristic information of the login user, specifically, if the user does not read the recommendation rule set or stored by the user after logging in, the system acquires the recommendation rule of the same type of user according to the user information through a big data analysis method and gives the user, further, if the same type of user is not found, generating the recommendation rule of the user according to a default rule of the system, preferably, the default recommendation rule of the system is a public legal service person who recommends the same type, is similar in age, is in the same region, has a high job level, has the same industry background, has served the user once, has the same type of service provided and the sought type of service, has a professional speciality, and has a high satisfaction rate and/or service level.
Further, the recommendation rule is composed of a recommendation algorithm rule matrix, and the recommendation algorithm rule matrix includes recommendation parameter information, recommendation participation information and/or recommendation weight information, and the like, where the recommendation parameter information includes event parameter information, calculation parameter information, and the like.
Wherein, the item parameter information can be divided into gender (p1), age stage (p2), geographic information (p3), job level (p4), industry background (p5), service history (p6), service type (p7), specialty (p8) and the like; the calculation parameter information can be divided into a satisfaction rate (p9), a service level and the like; whether to participate in the recommendation information refers to whether to participate in the recommendation algorithm of the user; the recommendation weight information is the calculation weight W (n) of a certain recommendation parameter in recommendation, and the importance of the recommendation parameter is judged according to the weight calculation value.
For example, after logging in a user with loginID of loginId _1, the system does not acquire the recommendation rule of related legal service personnel and does not find the recommendation rule of the same kind of user, the system presets the recommendation rule of the user according to the default condition, namely, generating (loginId _1, gender p1, participation recommendation calculation, 0.8; loginId _1, age stage p2, participation recommendation calculation, 0.9; loginId _1, geographic position p3, participation recommendation calculation, 1.2; loginId _1, job level p4, participation recommendation calculation, 0.1; loginId _1, industry background p5, participation recommendation calculation, 0.5; loginId _1, service history p6, participation recommendation calculation, 1.1; loginId _1, recommendation calculation type p7, participation recommendation calculation, 2.0; loginId _1, professional direction p8, participation recommendation calculation, recommendation calculation rate of loginId _1, recommendation calculation rate p 351, recommendation calculation, recommendation rate of loginId _1, recommendation calculation, recommendation rate p10, 0.1; ) The recommended algorithm rule matrix of (1).
And S130, calculating the recommendation index of the legal service personnel according to the recommendation rule.
In order to recommend professional and suitable public legal service personnel to a user more accurately and individually, in this embodiment, a recommendation rule of the user is first generated or obtained, then online legal service personnel on a public legal service platform are extracted, the user who has served the user in the past and is evaluated as unsatisfactory is eliminated, then according to the recommendation rule, a data tag of the legal service personnel is substituted into the recommendation rule of the user for weighted calculation, so that a recommendation index of the legal service personnel relative to the user is obtained, and a specific calculation formula is as follows:
wherein R is a recommendation index of legal service personnel;
Pkis a matter parameter;
Lmis PkSpecific information of the item parameters;
UPkrecommending item parameters in the rules for the user;
n is PkThe number of transaction parameters;
Sito calculate the parameters;
Ciis SiCalculating an arithmetic factor of the parameter;
j is SiCalculating the number of parameters;
f is whether to participate in the calculation value, if participate in the calculation, the F is 1; if not, taking 0 as F;
Wkitem parameter UP for a user recommendation rulekA calculated weight in the legal service personnel recommendation;
Wito calculate a parameter SiCalculating a weight in the legal service personnel recommendation.
The following is illustrated by way of example:
for example, the legal service personnel who are online and idle in the platform have C1, C2, preferably, the C2 to C2 may be legal service personnel who are newly registered to be online or recommended personnel obtained according to the original recommendation algorithm, and are not specifically limited, first, the label data of the C2, the legal service personnel C2 required by the user is removed according to the application feature of the user, then, the unsatisfactory C2 is removed according to the historical legal service personnel list and the satisfaction evaluation, and then the legal service personnel list recommended to the user is C2, so as to obtain the label information of each personnel in the service personnel list, and a two-dimensional label data matrix of the legal service personnel is formed by { 2: 2, 2{ 2, 2{ 2} 2, 2{ 2} 2{ 2} 3{ 2, 2{ 2} } 3{ 2,
{ C: t { 12}, t {23 }, t { 036}, t { 143}, t { 255, 357, 458}, t { }, t { 671, 772}, t { 881, 984, 85}, t { 092}, t { 1101} }, { C: t { 212}, t { 321}, t { 431}, t { 541}, t { 652, 753}, t { 8699}, t { 971, 73, 075}, t { 182, t, 384, 485}, t { 593}, t { }, C: t { 711}, t { 823}, t { 931}, t { 42}, t { 053, 155}, t { 2669}, t { 371, 472, t }, t { 681, 782, 883, 85, t { 91}, t { 1028}, t { 13, t } of an economic service person, t {95 } of an economic, a business-oriented rule, a business related, business.
Further, the obtained two-dimensional label data matrix of the legal service personnel is brought into the obtained user recommendation algorithm rule for matching calculation to obtain a recommended index value of the legal service personnel, namely:
the recommended index value is calculated by taking a legal service person C4 as an example, and the recommended index value R is equal to count [ ' woman ' in ' woman ' ] × × 0.8+ count [ ' youth ' in ' youth [ ] ×.9+ count [ ' long sand ' in ' × ×.2+ count [ ' middle grade ' in ' assistant, primary, middle grade, secondary master, expert ' ] × 41 × 50.5+ count [ ' education industry, cultural industry ' in ' education industry ' ] × ×.5+ count [ ' 99 ' in ' 23,58,99 ' ] × 4691.1 + count ' consult, mediate consult, consult ' in ' law ' ] 5 581 [ ' ×.0+ count, economic law ' ] ×.1, 24, international ' 94 ', 95.1 + count [/593, 94.5 ' ] and 94 ' ] No longer refer to the legal regulation, No. 5 ' ] ×, 2.0+ 54, 24, No. 9, No. 5 ' ] colligative law, No. 3, No. 5 ' ] is included in the law, No. 3, No. 5, No. 3, No. 9, No. 3, No. 2, No. 5, No. 3, No. 5, No. 9, No. 2.
And S140, recommending legal service personnel to the login user according to the legal service personnel recommendation index.
Preferably, the recommendation indexes of the legal service personnel obtained in step S130 can be arranged in a descending order, and the information can be displayed or pushed to the user according to the arrangement order. For example, the recommendation index of each legal service person is calculated according to the rules, then 5 legal service persons are selected according to the arrangement sequence from large to small and recommended to the user, and the recommendation list is displayed on the user interface, and further, the user can select the legal service persons to provide services for the legal service persons according to personal preference.
Further, after recommending legal service personnel to the login user according to the legal service personnel recommendation index, the method further comprises the following steps:
analyzing the push information, if the push information is adopted, increasing the calculation weight W according to a preset valuekAnd/or WiAnd meanwhile, updating the recommendation rule of the user.
After the system recommends the recommended legal service personnel to the user, whether the recommended legal service personnel is adopted by the user or not is continuously tracked, and if the recommended legal service personnel is adopted by the user, the calculation weight W is increased according to a preset numerical valuekAnd/or Wi(e.g., 0.1) while updating the legal service personnel recommendation algorithm rule matrix information for the user, preferably the "adoption" includes a collection or a handling operation.
Further, after legal service is completed, the user can evaluate the satisfaction degree of the service of legal service personnel, and after the evaluation is completed, the system updates the satisfaction rate and the service level label data of the legal service personnel. When the user logs in again to seek legal service, the recommendation indexes of the legal service personnel are calculated according to the latest legal service personnel recommendation algorithm rule matrix, the recommendation indexes are arranged according to the descending order, and a legal service personnel recommendation list is pushed to the user according to the arrangement order.
According to the legal service personnel recommendation method based on the public legal service platform, public legal services which are more accurate, more suitable and higher in quality can be provided for the vast social public through the public legal service personnel intelligent recommendation algorithm based on big data. Furthermore, the satisfaction degree of the social public on the legal service can be improved, meanwhile, public legal service employees can be promoted to continuously improve the self service capability, and the construction of legal service talents and teams is facilitated. Through good service experience of one time and another time, the method promotes the public to learn law and use, adopts the law means to solve the problem habit, improves the law curing literacy, and is beneficial to the construction of the law curing country.
FIG. 2 illustrates a block diagram of a public legal services platform based legal service personnel recommendation device 200 according to the present application. As shown in fig. 2, the apparatus 200 includes:
an obtaining module 210, configured to obtain personal information and application feature information of a login user;
the judging module 220 is configured to judge whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
a calculating module 230, configured to calculate a recommendation index of legal service personnel according to the recommendation rule;
the pushing module 240 is configured to recommend legal service personnel to the login user according to the legal service personnel recommendation index.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
For example, without limitation, exemplary types of hardware logic that may be used include Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOC), load programmable logic devices (CP L D), and so forth.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A legal service personnel recommendation method based on a public legal service platform is characterized by comprising the following steps:
acquiring personal information and application characteristic information of a login user;
judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
calculating a recommendation index of legal service personnel according to the recommendation rule;
and recommending legal service personnel to the login user according to the legal service personnel recommendation index.
2. The method of claim 1,
the personal information includes unique identification information;
the application characteristic information comprises legal consultation and/or transaction information.
3. The method of claim 2, wherein obtaining the existing recommendation rule comprises:
and acquiring the recommendation rule of the user according to the unique identification information of the login user.
4. The method of claim 3,
the recommendation rule is composed of a recommendation algorithm rule matrix;
the recommendation algorithm rule matrix comprises recommendation parameter information, recommendation participation information and/or recommendation weight information.
5. The method of claim 4, wherein the recommended parameter information includes event parameter information and calculation parameter information.
6. The method of claim 5, wherein said calculating a legal service personnel recommendation index according to the recommendation rule comprises:
acquiring online legal service personnel information on a public legal service platform;
screening the legal service personnel information according to the personal information and the application characteristic information of the login user;
and calculating to obtain the recommendation index of the legal service personnel according to the recommendation rule associated with the screened legal service personnel information.
7. The method of claim 6, wherein the legal service personnel recommendation index is calculated using the formula:
wherein R is a recommendation index of legal service personnel;
Pkis a matter parameter;
Lmis PkSpecific information of the item parameters;
UPkrecommending item parameters in the rules for the user;
n is PkThe number of transaction parameters;
Sito calculate the parameters;
Ciis SiComputingAn arithmetic factor of the parameter;
j is SiCalculating the number of parameters;
f is whether to participate in the calculation value, if participate in the calculation, the F is 1; if not, taking 0 as F;
Wkitem parameter UP for a user recommendation rulekA calculated weight in the legal service personnel recommendation;
Wito calculate a parameter SiCalculating a weight in the legal service personnel recommendation.
8. The method of claim 7, wherein said recommending legal service personnel to the logged-in user according to the legal service personnel recommendation index comprises:
arranging the screened legal service personnel from large to small according to the recommendation index of the legal service personnel;
and displaying and/or sending information to the user according to the arrangement sequence.
9. The method of claim 8, further comprising, after recommending legal service personnel to the logged-in user according to the legal service personnel recommendation index:
analyzing the push information, if the push information is adopted, increasing the calculation weight W according to a preset valuekAnd/or WiAnd meanwhile, updating the recommendation rule of the user.
10. A legal service personnel recommendation device based on a public legal service platform is characterized by comprising:
the acquisition module is used for acquiring personal information and application characteristic information of a login user;
the judging module is used for judging whether the recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
the calculation module is used for calculating the recommendation index of the legal service personnel according to the recommendation rule;
and the pushing module is used for recommending the legal service personnel to the login user according to the legal service personnel recommendation index.
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