CN111460280B - Legal service personnel recommendation method based on public legal service platform - Google Patents
Legal service personnel recommendation method based on public legal service platform Download PDFInfo
- Publication number
- CN111460280B CN111460280B CN202010115382.8A CN202010115382A CN111460280B CN 111460280 B CN111460280 B CN 111460280B CN 202010115382 A CN202010115382 A CN 202010115382A CN 111460280 B CN111460280 B CN 111460280B
- Authority
- CN
- China
- Prior art keywords
- recommendation
- legal
- information
- legal service
- service personnel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000004364 calculation method Methods 0.000 claims description 32
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000012216 screening Methods 0.000 claims description 3
- 239000000758 substrate Substances 0.000 claims 1
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000009223 counseling Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063112—Skill-based matching of a person or a group to a task
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services; Handling legal documents
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the disclosure provides a legal attendant recommending 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 recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 legal attendant recommendation index 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, thereby improving the service quality of the public legal service personnel.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of information technology, and more particularly, to a legal attendant recommendation method and apparatus based on a public legal service platform.
Background
With the development and popularization of internet technology, various public legal service platforms are emerging.
Currently, most legal service platforms accomplish the docking of the public with the practitioners who provide legal services in the following two ways, namely: the platform carries out random matching on the public searching legal service and public legal service personnel on the platform which are on line and in an idle state; mode two: when the public initiates legal service requests on the platform, online public legal service personnel provide legal services in a robbery form.
It can be seen that a mechanical, random approach is adopted by the matching between the public of the society on existing platforms, which is in need of legal services, and the legal practitioners who are in need of legal services. Therefore, a plurality of problems are exposed, on one hand, random matching or robbery order matching can be carried out, the problems that the professional field is not matched or the laws and regulations have regional differences and the like can exist, and legal service personnel can not provide satisfactory and high-quality legal service for the public; on the other hand, although legal service staff can select the service requirement of own professional scope when robbing the bill, once the situation that the management system mechanism is not sound and matched occurs, the situation that no one robs the bill and no one provides legal service occurs.
In summary, whether the quality of the legal service provided is low or the legal service cannot be provided in time, the public legal service which is common Hui Jundeng, efficient and convenient is affected by people.
Disclosure of Invention
According to the embodiment of the disclosure, aiming at the problems, the method and the device for recommending the legal service personnel based on the public legal service platform are provided, so that more intelligent and personalized public legal service personnel recommendation can be realized.
In a first aspect of the present disclosure, a legal attendant recommendation method based on a public legal service platform 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 recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 legal attendant recommendation index 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 includes legal consultation and/or transaction information.
Further, the acquiring 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 consists of a recommendation algorithm rule matrix;
the recommendation algorithm rule matrix comprises recommendation parameter information, whether to participate in recommendation information and/or recommendation weight information.
Further, the recommended parameter information includes item parameter information and calculation parameter information.
Further, the calculating the recommendation index of legal attendant according to the recommendation rule includes:
acquiring on-line legal service personnel information on a public legal service platform;
screening the legal service personnel information according to personal information and application characteristic information of the login user;
and calculating to obtain the recommendation index of the legal attendant according to recommendation rules associated with the screened legal attendant information.
Further, the legal attendant recommendation index is calculated using the following formula:
wherein R is a recommendation index of legal service personnel;
P k is a matter parameter;
L m is P k Specific information of item parameters;
UP k recommending item parameters in the rule for the user;
n is P k Number of transaction parameters;
S i for calculating parameters;
C i is S i Calculating an arithmetic factor of the parameter;
j is S i Calculating the number of parameters;
f is whether to participate in calculation values, if so, F takes 1; if the calculation is not participated, F takes 0;
W k item parameter UP for recommending rules for user k Calculating weights in the legal attendant recommendations;
W i to calculate the parameter S i The weights are calculated in the legal attendant recommendations.
Further, the recommending legal attendant to the logged-in user according to the legal attendant 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 transmitting 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:
analyzing the push information, if the push information is adopted, increasing the calculation weight W according to a preset value k And/or W i And updating the recommendation rule of the user at the same time.
In a second aspect of the present disclosure, there is provided a legal attendant recommending apparatus based on a public legal service platform, 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 recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 calculating module is used for calculating the recommendation index of legal service personnel according to the recommendation rule;
and the pushing module is used for recommending legal service personnel to the login user according to the legal service personnel recommendation index.
According to the legal service personnel recommending method based on the public legal service platform, corresponding recommending rules are obtained through analyzing the information of the login user, legal service personnel recommending indexes are calculated according to the recommending rules, legal service personnel are recommended to the login user according to the legal service personnel recommending indexes, 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, satisfaction rate of people to public legal service is improved, and national law-governing social construction is assisted.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 is a flow chart of a legal attendant recommendation method based on a public legal service platform in accordance with the present application;
fig. 2 is a block diagram of a legal attendant recommending apparatus based on a common legal service platform according to the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
FIG. 1 illustrates a flow chart of a legal attendant recommendation method 100 based on a public legal service platform in accordance with the present application. As shown in FIG. 1, comprises
S110, personal information and application characteristic information of the login user are obtained.
Personal information and application characteristic information of a login user are obtained through a mobile application.
Personal information of a logged-in user is acquired to generate recommendation rules according to user tags, wherein the user tags comprise gender, age stage, region, education level, industry, historical legal service personnel codes and the like of the user.
When a user registers on the public legal service platform, the information of an account number (necessary to be filled), an identity card number (necessary to be filled), a contact phone, a frequent address, education level, industry and the like, namely personal information of the user, is required to be filled. Meanwhile, the system calculates the age stage of the user according to the identity card number filled in by the user. After successful registration, the system stores user personal information such as user account number (user unique identification information) and the like in the server, and further, after the user selects legal service, the system stores coding information of legal service provider in the server. When the user logs in again, the system can acquire personal information of the logged-in user from the server according to the user account.
Further, the obtained information composition may be loginnid (unique identification information of the user), gender, age stage, region where the user is located, education level, industry where the user belongs, a history legal service personnel code list, etc., further, the history legal service personnel code list is legal service personnel information recorded in a list form and used for providing legal service for the user, further, commas are used for separating each information field, and a semicolon is used for separating each label value in the same information field. For example, (Login ID, UP1{ girl }, UP2{ young }, UP3{ Changsha }, UP4{ family }, UP5{ education industry }, UP6{23;58;99 }), i.e., indicating that the Login ID user is young women, changsha, academic, education industry, three legal service personnel (coded as 23,58, 99) have been provided for legal service.
The application characteristic information of the user is acquired to further generate legal attendant recommendation rules according to service matters required by the user.
Further, after the user logs into the public legal service platform, legal consultation may need to be sought and/or public legal service matters may need to be applied for. Specifically, when the user carries out legal consultation, the user can carry out consultation aiming at a certain type of transaction; when the user is transacting public legal service matters, a specific transaction is selected. For example: the user needs to make legal consultations or questioning notarization, questioning authentication, etc. about the intellectual property related questions.
Further, after the user logs in, the user first selects from the service major classes (legal consultation or transaction), and then selects the service minor class corresponding to the user according to the own needs. Further, the service subclass of the law consultation major class may be marital family consultation, employee dispute consultation, trademark patent consultation, intellectual property consultation, corporate financial tax consultation, notarization consultation, identification consultation, and the like. The service subclasses of the transaction major class may be office notations, office judicial authentication, law aid, office mediation, office agents, 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 consulting service, and then selects the consultation intellectual property related problem, the obtained user application characteristic information is (loginID, UP7{ legal consultation }, UP8{ intellectual property }), and the obtained user application characteristic information is combined with the personal information of the user to be: (loginID, UP1{ girl }, UP2{ young }, UP3{ Changsha }, UP4{ family }, UP5{ education industry }, UP6{23;58;99}, UP7{ legal consultation }, UP8{ intellectual property }).
S120, judging whether recommendation rules of the login user exist according to the personal information of the login user; if the recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does not exist, generating the recommendation rule of the login user 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 through personal information of the logged-in user. If yes, directly calling the existing recommendation rule according to the unique identification information of the login user; if the user does not find the same kind of users, the recommendation rule of the user is generated according to the default rule of the system, and preferably, the default recommendation rule of the system is a public legal service personnel with the same identity, similar age, same area, high job level, same industry background, once served users, consistent service type with the sought service type, professional specialty, consistent satisfaction rate and/or high service level.
Further, the recommendation rule is composed of a recommendation algorithm rule matrix, the recommendation algorithm rule matrix comprises recommendation parameter information, whether to participate in recommendation information, recommendation weight information or not, and the like, wherein the recommendation parameter information comprises item parameter information, calculation parameter information and the like.
The item parameter information may be classified into gender (p 1), age (p 2), geographic information (p 3), job level (p 4), industry background (p 5), service history (p 6), service type (p 7), expertise (p 8), etc.; the calculated parameter information may be classified into a satisfaction rate (p 9), a service level, etc.; whether to participate in the recommendation information refers to whether to participate in a recommendation algorithm of the user; the recommendation weight information is a calculation weight W (n) for recording a certain analogized parameter in recommendation, and the importance of the recommendation parameter is judged according to the weight calculation value.
For example, after logging in the system with the logindid of logindid_1, the system will preset the recommendation rule of the user according to the default condition if the related legal serviceman recommendation rule is not obtained and the recommendation rule of the same kind of user is not found, that is, generate (logindid_1, gender p1, participation recommendation calculation, 0.8; logindid_1, age stage p2, participation recommendation calculation, 0.9; logindid_1, geographic position p3, participation recommendation calculation, 1.2; logindid_1, job stage p4, participation recommendation calculation, 0.1; logindid_1, industry background p5, participation recommendation calculation, 0.5; logiid_1, service history p6, participation recommendation calculation, 1.1; logindid_1, service type p7, participation recommendation calculation, 2.0; logiid_1, direction p8, participation recommendation calculation, 1.1, logindid_1, participation recommendation calculation, job stage p9, participation calculation, 1.2; logindid_1, professional rule matrix calculation, and satisfaction matrix calculation.
S130, calculating a legal attendant recommendation index according to the recommendation rule.
In order to more accurately and individually recommend professional and suitable public legal service personnel to a user, in the embodiment, firstly, a recommendation rule of the user is generated or obtained, then, online legal service personnel on a public legal service platform are extracted, the user which has been serviced in the past and is evaluated as unsatisfactory is removed, then, according to the recommendation rule, a data tag of the legal service personnel is substituted into the recommendation rule of the user to carry out weighted calculation, and a recommendation index of the legal service personnel relative to the user is obtained, wherein a specific calculation formula is as follows:
wherein R is a recommendation index of legal service personnel;
P k is a matter parameter;
L m is P k Parameters of mattersSpecific information;
UP k recommending item parameters in the rule for the user;
n is P k Number of transaction parameters;
S i for calculating parameters;
C i is S i Calculating an arithmetic factor of the parameter;
j is S i Calculating the number of parameters;
f is whether to participate in calculation values, if so, F takes 1; if the calculation is not participated, F takes 0;
W k item parameter UP for recommending rules for user k Calculating weights in the legal attendant recommendations;
W i to calculate the parameter S i The weights are calculated in the legal attendant recommendations.
The following is illustrative:
for example, legal service personnel available online in the platform are C1, C2, C3, C4, C5, C6. Preferably, the C1 to C6 may be newly registered online legal service personnel or recommended personnel obtained according to an original recommendation algorithm, which is not limited herein. Firstly, acquiring tag data of C1, C2, C3, C4, C5 and C6, then eliminating legal service personnel C2 which are not included in the service type of a user according to the application characteristics of the user, and eliminating unsatisfied C6 according to a historical legal service personnel list and satisfaction evaluation, wherein the legal service personnel list required to be recommended to the user is C1, C3, C4 and C5. Obtaining label information of each person in the attendant list to form a two-dimensional label data matrix of legal attendant, namely [ { C1:t1{ L11}, t2{ L21}, t3{ L31}, t4{ L41}, t5{ L51, L52, L53}, t6{ L66, L69, L612}, t7{ L71, L73, L75}, t8{ L82, L83, L84}, t9{ L91}, t10{ L101},
{ C3: t1{ L12}, t2{ L23}, t3{ L36}, t4{ L43}, t5{ L55, L57, L58}, t6{ L6112}, t7{ L71, L72}, t8{ L81, L84, L85}, t9{ L92}, t10{ L101}, and { C4 }; t1{ L12}, t2{ L21}, t3{ L31}, t4{ L41}, t5{ L52, L53}, t6{ L699}, t7{ L71, L73, L75}, t8{ L82, L83, L84, L85}, t9{ L93}, t10{ L1043}, { C5: t1{ L11}, t2{ L23}, t3{ L31}, t4{ L42}, t5{ L53, L55}, t6{ L669}, t7{ L71, L72, L76}, t8{ L81, L82, L83, L85, t9{ L91}, t10{ L1028}, and taking a C4 law attendant as an example, obtaining a two-dimensional tag data matrix L (m) of its personnel attributes, said L (m) information being: { C4:t1{ girl }, t2{ young }, t3{ Changsha }, t4{ middle-level }, t5{ education industry; cultural industry }, t6{99}, t7{ legal consultation; legal assistance; mediation }, t8{ civil business; administrative laws; an economic process; foreign traffic, t9{95% }, t10{3 star }.
Further, the obtained legal service personnel two-dimensional label data matrix is brought into the obtained user recommendation algorithm rule, and matching calculation is carried out, so that the legal service personnel recommendation index value is obtained, namely:
calculating the person recommendation index value r= =count [ ' girl ' in ' girl ' ] ×1×0.8+count [ ' young ' in ' young; middle-aged ' ] ×1×0.9+count [ ' Changsha ' in ' Changsha ' ] ×1×1.2+count [ ' intermediate ' in ' assistant, primary, intermediate, secondary, primary, expert ' ] ×1×0.5+count [ ' education industry, cultural industry ' in ' education industry ' ] ×1×0.5+count [ '99' in '23,58,99' ] ×1×1.1+count [ ' legal counseling, legal assistance, mediation ' in ' legal counseling ' ] ×1×2.0+count [ ' civil law, administrative law, economic law, foreign-of-origin ' in ' civil law ' ] ×1×1.1+ [ '95'/100] ×1×1.2+ [ '3'/10] ×1×1.0=9.54. Similarly, the recommendation indexes of C1, C3, and C5 can be calculated according to the same rule, and will not be described herein.
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 attendant obtained in step S130 may be arranged in order from large to small, and information may be displayed or pushed to the user according to the arrangement order. For example, the recommendation index of each legal attendant is calculated according to the rule, then 5 legal attendant is selected according to the arrangement sequence from large to small to be recommended to the user, and the recommendation list is displayed on the user interface at the same time, and further, the user can select legal attendant to provide service 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:
analyzing the push information, if the push information is adopted, increasing the calculation weight W according to a preset value k And/or W i And updating the recommendation rule of the user at the same time.
After the system recommends the recommended legal service personnel to the user, the system will continuously track whether the recommended legal service personnel are adopted by the user, and if so, the computing weight W is increased according to a preset value k And/or W i (e.g., 0.1) while updating the user's legal attendant recommendation algorithm rule matrix information, preferably, the "adoption" includes collection or transacted operations.
Further, after legal service is completed, the user can evaluate the satisfaction degree of the legal service personnel, and after the evaluation is completed, the system updates the satisfaction rate and service grade label data of the legal service personnel. When the user logs in again and seeks legal service, the recommendation index of each legal service person is calculated according to the latest legal service person recommendation algorithm rule matrix, the recommendation indexes are arranged according to the sequence from large to small, and a legal service person recommendation list is pushed to the user according to the arrangement sequence.
According to the legal attendant recommending method based on the public legal attendant platform, through an intelligent recommending algorithm of the public legal attendant based on big data, more accurate, more suitable and higher-quality public legal service can be provided for the vast public. Further, satisfaction of the public to legal services can be improved, meanwhile, public legal service practitioners can be promoted to continuously improve self service capacity, and legal service talents and team construction are facilitated. Through the good service experience of the first time, the method for using the mass people to solve the problems by using legal means is promoted, the literacy of law is improved, and the construction of the law-cured country is facilitated.
Fig. 2 shows a block diagram of a legal attendant recommending apparatus 200 based on a public legal service platform 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;
a judging module 220, configured to judge whether a recommendation rule of the login user exists according to the personal information of the login user; if the recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 legal attendant recommendation index according to the recommendation rule;
and the pushing module 240 is configured to recommend legal service personnel to the login user according to the legal service personnel recommendation index.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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.
Moreover, although 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. In 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 limiting the scope of the present 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 example forms of implementing the claims.
Claims (6)
1. The legal attendant recommending method based on the public legal service platform is characterized by comprising the following steps of:
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 recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 legal attendant recommendation index according to the recommendation rule;
recommending legal service personnel to the login user according to the legal service personnel recommendation index;
the calculating the recommendation index of legal service personnel according to the recommendation rule comprises the following steps:
acquiring on-line legal service personnel information on a public legal service platform;
screening the legal service personnel information according to personal information and application characteristic information of the login user;
calculating to obtain a recommendation index of the legal service personnel according to recommendation rules associated with the screened legal service personnel information;
wherein the recommendation rule is composed of a recommendation algorithm rule matrix;
the recommendation algorithm rule matrix comprises recommendation parameter information, whether to participate in recommendation information and/or recommendation weight information;
the recommended parameter information comprises item parameter information and calculation parameter information;
the legal attendant recommendation index is calculated using the following formula:
wherein R is a recommendation index of legal service personnel;
P k is a matter parameter;
L m is P k Specific information of item parameters;
UP k recommending item parameters in the rule for the user;
n is P k Number of transaction parameters;
S i for calculating parameters;
C i is S i Calculating an arithmetic factor of the parameter;
j is S i Calculating the number of parameters;
f is whether to participate in calculation values, if so, F takes 1; if the calculation is not participated, F takes 0;
W k item parameter UP for recommending rules for user k Calculating weights in the legal attendant recommendations;
W i to calculate the parameter S i The weights are calculated in the legal attendant recommendations.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the personal information includes unique identification information;
the application characteristic information includes legal consultation and/or transaction information.
3. The method of claim 2, wherein the obtaining the existing recommendation rules comprises:
and acquiring the recommendation rule of the user according to the unique identification information of the login user.
4. The method of claim 1, wherein said recommending legal service personnel to the logged-in user based on 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 transmitting information to the user according to the arrangement sequence.
5. The method of claim 4, further comprising, after recommending legal service personnel to the logged-in user based on 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 value k And/or W i And updating the recommendation rule of the user at the same time.
6. Legal attendant recommending device based on public legal service platform, 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 recommendation rule exists, acquiring the existing recommendation rule, and if the recommendation rule does 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 calculating module is used for calculating the recommendation index of legal service personnel according to the recommendation rule;
the pushing module is used for recommending legal service personnel to the login user according to the legal service personnel recommendation index;
the computing module is specifically used for:
acquiring on-line legal service personnel information on a public legal service platform;
screening the legal service personnel information according to personal information and application characteristic information of the login user;
calculating to obtain a recommendation index of the legal service personnel according to recommendation rules associated with the screened legal service personnel information;
wherein the recommendation rule is composed of a recommendation algorithm rule matrix;
the recommendation algorithm rule matrix comprises recommendation parameter information, whether to participate in recommendation information and/or recommendation weight information;
the recommended parameter information comprises item parameter information and calculation parameter information;
the legal attendant recommendation index is calculated using the following formula:
wherein R is a recommendation index of legal service personnel;
P k is a matter parameter;
L m is P k Specific information of item parameters;
UP k recommending item parameters in the rule for the user;
n is P k Number of transaction parameters;
S i for calculating parameters;
C i is S i Calculating an arithmetic factor of the parameter;
j is S i Calculating the number of parameters;
f is whether to participate in calculation values, if so, F takes 1; if the calculation is not participated, F takes 0;
W k item parameter UP for recommending rules for user k Calculating weights in the legal attendant recommendations;
W i to calculate the parameter S i The weights are calculated in the legal attendant recommendations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010115382.8A CN111460280B (en) | 2020-02-25 | 2020-02-25 | Legal service personnel recommendation method based on public legal service platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010115382.8A CN111460280B (en) | 2020-02-25 | 2020-02-25 | Legal service personnel recommendation method based on public legal service platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111460280A CN111460280A (en) | 2020-07-28 |
| CN111460280B true CN111460280B (en) | 2023-10-24 |
Family
ID=71679969
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010115382.8A Active CN111460280B (en) | 2020-02-25 | 2020-02-25 | Legal service personnel recommendation method based on public legal service platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111460280B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102622375A (en) * | 2011-02-01 | 2012-08-01 | 上海百事通信息技术有限公司 | Intelligent matching system and method for third-party lawyer recommendations |
| CN108596797A (en) * | 2018-04-30 | 2018-09-28 | 于海松 | A kind of legal advice service platform public platform |
| CN108681548A (en) * | 2018-03-27 | 2018-10-19 | 成都律云科技有限公司 | A kind of lawyer's information processing method and system |
| JP2018169870A (en) * | 2017-03-30 | 2018-11-01 | Ｈｒソリューションズ株式会社 | Recommendation information notifying device, method and program |
| KR20190023321A (en) * | 2017-08-28 | 2019-03-08 | 리걸테크 주식회사 | Apparatus and Method for Providing Lawyer Law Service |
| CN109710845A (en) * | 2018-12-25 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | Information recommended method, device, computer equipment and readable storage medium storing program for executing |
| CN110019429A (en) * | 2017-12-01 | 2019-07-16 | 上海百事通信息技术股份有限公司 | Legal services system, method, server, equipment and medium |
| CN110377819A (en) * | 2019-06-17 | 2019-10-25 | 平安科技（深圳）有限公司 | Arbitrator's recommended method, device and computer equipment based on big data |
| CN110825960A (en) * | 2019-10-08 | 2020-02-21 | 中通服创发科技有限责任公司 | Learning content recommendation method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030187691A1 (en) * | 2002-03-28 | 2003-10-02 | Health Net, Inc. | Method and system for matching a service seeker with a service provider |
| CN107368488A (en) * | 2016-05-12 | 2017-11-21 | 阿里巴巴集团控股有限公司 | A kind of method for determining user behavior preference, the methods of exhibiting and device of recommendation information |
| CN113259833A (en) * | 2020-02-10 | 2021-08-13 | 南京理工大学 | Subway station auxiliary traveling system and method based on visually impaired passengers |
-
2020
- 2020-02-25 CN CN202010115382.8A patent/CN111460280B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102622375A (en) * | 2011-02-01 | 2012-08-01 | 上海百事通信息技术有限公司 | Intelligent matching system and method for third-party lawyer recommendations |
| JP2018169870A (en) * | 2017-03-30 | 2018-11-01 | Ｈｒソリューションズ株式会社 | Recommendation information notifying device, method and program |
| KR20190023321A (en) * | 2017-08-28 | 2019-03-08 | 리걸테크 주식회사 | Apparatus and Method for Providing Lawyer Law Service |
| CN110019429A (en) * | 2017-12-01 | 2019-07-16 | 上海百事通信息技术股份有限公司 | Legal services system, method, server, equipment and medium |
| CN108681548A (en) * | 2018-03-27 | 2018-10-19 | 成都律云科技有限公司 | A kind of lawyer's information processing method and system |
| CN108596797A (en) * | 2018-04-30 | 2018-09-28 | 于海松 | A kind of legal advice service platform public platform |
| CN109710845A (en) * | 2018-12-25 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | Information recommended method, device, computer equipment and readable storage medium storing program for executing |
| CN110377819A (en) * | 2019-06-17 | 2019-10-25 | 平安科技（深圳）有限公司 | Arbitrator's recommended method, device and computer equipment based on big data |
| CN110825960A (en) * | 2019-10-08 | 2020-02-21 | 中通服创发科技有限责任公司 | Learning content recommendation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111460280A (en) | 2020-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20190156426A1 (en) | Systems and methods for collecting and processing alternative data sources for risk analysis and insurance | |
| CN109345417B (en) | Online assessment method and terminal equipment for business personnel based on identity authentication | |
| US20210182859A1 (en) | System And Method For Modifying An Existing Anti-Money Laundering Rule By Reducing False Alerts | |
| CN107832468A (en) | Demand recognition methods and device | |
| CN110659985A (en) | Method and device for fishing back false rejection potential user and electronic equipment | |
| Hartley et al. | Examining prosecutorial discretion in federal criminal cases: Legal and extra-legal determinants of declination and charge change decisions | |
| US10740824B2 (en) | Product delivery system and method | |
| WO2019144035A1 (en) | Systems and methods for collecting and processing alternative data sources for risk analysis and insurance | |
| CN111062774B (en) | Activity delivery method and device, electronic equipment and computer readable medium | |
| CN111460280B (en) | Legal service personnel recommendation method based on public legal service platform | |
| CN116894498A (en) | Training method, strategy identification method, device and equipment of network model | |
| CN115795345A (en) | Information processing method, device, equipment and storage medium | |
| CN116091249A (en) | Transaction risk assessment method, device, electronic equipment and medium | |
| CN113723974A (en) | Information processing method, device, equipment and storage medium | |
| JP4045835B2 (en) | Decision support method | |
| KR102123764B1 (en) | Post service management method | |
| CN113327161A (en) | Intelligent wind control decision system for credit business | |
| US20200125325A1 (en) | Identification of users across multiple platforms | |
| CN111523034A (en) | Application processing method, device, equipment and medium | |
| US20190304040A1 (en) | System and Method for Vetting Potential Jurors | |
| CN116629639B (en) | Evaluation information determining method and device, medium and electronic equipment | |
| KR102643045B1 (en) | Chatbot service providing system for analyzing by time and method thereof | |
| US11822562B2 (en) | Unstructured text processing for geographical location determination system | |
| CN113450067B (en) | Risk control method, device and system based on decision engine and electronic device | |
| CN116226240A (en) | Business data display method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |