CN105955961A - Reservation information processing method and apparatus - Google Patents
Reservation information processing method and apparatus Download PDFInfo
- Publication number
- CN105955961A CN105955961A CN201610301168.5A CN201610301168A CN105955961A CN 105955961 A CN105955961 A CN 105955961A CN 201610301168 A CN201610301168 A CN 201610301168A CN 105955961 A CN105955961 A CN 105955961A
- Authority
- CN
- China
- Prior art keywords
- expert
- information
- student
- good
- reservation
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- 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
- G06Q10/02—Reservations, e.g. for tickets, services or events
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Probability & Statistics with Applications (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a reservation information processing method and apparatus. An embodiment of the method comprises the steps of receiving reservation information used for knowledge sharing and sent by a student client; calculating the similarity between student information and reservation expert information; in response to a condition that the similarity obtained by calculation is greater than a predetermined threshold, pushing the student information to an expert client of a reservation expert indicated by the reservation expert information for the reservation expert to receive or reject reservation of the student client through the expert client; in response to a condition that the similarity obtained by calculation is smaller than or equal to the predetermined threshold, for each expert in other experts except the reservation expert in candidate experts, calculating the similarity between the student information and expert information of the expert; selecting the candidate expert with the similarity greater than the predetermined threshold as a recommended expert; and pushing expert information of the recommended expert to the student client. According to the embodiment, the reservation information processing efficiency is improved.
Description
Technical field
The application relates to field of computer technology, is specifically related to Internet technical field, especially relates to
And subscription information treating method and apparatus.
Background technology
Knowledge sharing is preengage, and is occurred by the fast development along with the Internet novel to know
Know the appointment mode being shared as purpose.In knowledge sharing is preengage, student inputs the student of oneself
Recommended information and problem describe information, then browse the expert info that system provides, and from system
The expert provided select oneself expert interested preengage；The expert being reserved receives
After the reservation of student, the information of trainee of student can be checked, then decide whether by line
Meet mode under voice and/or line to contact, reach the purpose of knowledge sharing.
But student's recommended information of student's input and problem if to describe information untreated just straight
Sending and receiving give reservation expert check, it is more likely that student input student's recommended information and ask
It is uncorrelated that topic description information and expert are good at field, causes the time wasting expert to be audited.
Also make student's problem cannot obtain correct answer simultaneously.Reduce the effect of knowledge sharing reservation
Rate.
Existing knowledge sharing reservation is general uses manual examination and verification mode to judge the student that student inputs
Recommended information describes information with problem, and whether to be good at field to expert relevant.And manual examination and verification mode
Time-consuming long, examination & verification is slow, and the most existing knowledge sharing reservation there is information of trainee and expert is good at
Realm information does not mates, the problem that the treatment effeciency of knowledge sharing subscription information is low.
Summary of the invention
The purpose of the application is to propose the subscription information treating method and apparatus of a kind of improvement, comes
Solve the technical problem that background section above is mentioned.
First aspect, this application provides a kind of subscription information processing method, and described method includes:
Receive the subscription information for knowledge sharing that student's client sends, wherein, described reservation letter
Breath include information of trainee and reservation expert info, described information of trainee include student's recommended information and
Problem describes information, and described reservation expert info includes expert introduction information and expert's course description
Information；Calculate the similarity between described information of trainee and described reservation expert info；In response to
The similarity calculating gained is more than predetermined threshold, to the reservation indicated by described reservation expert info
The expert clients of expert pushes described information of trainee for described reservation expert by described expert
Client accepts or refuses the reservation of described student's client；In response to calculating the similar of gained
Degree is less than or equal to described predetermined threshold, special to other in addition to described reservation expert in candidate expert
Each expert in Jia, calculate between described information of trainee and the expert info of this expert is similar
Degree；Choose the similarity candidate expert more than described predetermined threshold as recommending expert；To described
The expert info of expert is recommended described in student's client push.
In certain embodiments, described method also includes: to the expert introduction information of expert and specially
Family's course description information carries out statistical analysis and/or semantic analysis, obtains at least one expert info
Key word；The expert that at least one expert info key word described imports training in advance is good at neck
Territory Matching Model carries out mating and obtains the expert of described expert and be good at realm information.
In certain embodiments, the described information of trainee of described calculating and described reservation expert info it
Between similarity, including: described information of trainee is carried out statistical analysis and/or semantic analysis, carries
Take at least one information of trainee key word；At least one information of trainee key word described is imported pre-
The student's type matching model first trained carries out mating the student's type information obtaining described student；
Calculate described student's type information and similarity that described reservation expert is good between realm information.
In certain embodiments, described by least one information of trainee key word described import in advance
Student's type matching model of training carries out mating the student's type information obtaining described student, bag
Include: at least one information of trainee key word described is imported student's type matching mould of training in advance
Type carries out mating the student's type information obtaining comprising student's type matching degree, wherein, described
Member's type matching degree is used for characterizing and determines student's class according at least one information of trainee key word described
The accuracy of type information；From each student's type information according to student's type matching degree by greatly to
Little order selects first predetermined number student's type information to form student's type of described student
Information.
In certain embodiments, described by least one expert info key word described import in advance
The expert of training is good at field Matching Model and carries out mating and obtain the expert of this expert and be good at field letter
Breath, including: the expert that at least one expert info key word described imports training in advance is good at
Field Matching Model carries out mating and obtains comprising expert and be good at the expert of field matching degree and be good at field
Information, described expert is good at field matching degree for characterizing according at least one expert info described
Key word determines that expert is good at the accuracy of realm information；It is good at realm information from each expert
Being good at the descending order of field matching degree according to expert selects the second predetermined number expert to arrogate to oneself
Long realm information forms the expert of this expert and is good at realm information.
Second aspect, this application provides a kind of subscription information processing means, and described device includes:
Receive unit, be configured to receive the subscription information for knowledge sharing that student's client sends,
Wherein, described subscription information includes information of trainee and reservation expert info, described information of trainee bag
Including student's recommended information and problem describes information, described reservation expert info includes that expert introduction is believed
Breath and expert's course description information；Computing unit, is configured to calculate described information of trainee and institute
State the similarity between reservation expert info；First push unit, is configured in response to calculating
The similarity of gained is more than predetermined threshold, to the reservation expert indicated by described reservation expert info
Expert clients push described information of trainee for described reservation expert by described expert client
Terminate by or refuse described student's client and preengage；Second push unit, is configured to ring
Described predetermined threshold should be less than or equal in the similarity calculating gained, described to candidate expert removes
The each expert in other experts outside reservation expert, calculates described information of trainee and this expert's
Similarity between expert info；Choose similarity to make more than the candidate expert of described predetermined threshold
For recommending expert；To the expert info recommending expert described in described student's client push.
In certain embodiments, described device also includes that expert is good at realm information signal generating unit,
It is configured to: expert introduction information and expert's course description information to expert carry out statistical analysis
And/or semantic analysis, obtain at least one expert info key word；By at least one expert described
Information key imports the expert of training in advance and is good at field Matching Model and carries out coupling and obtain described
The expert of expert is good at realm information.
In certain embodiments, described computing unit includes: extraction module, is configured to institute
State information of trainee and carry out statistical analysis and/or semantic analysis, extract at least one information of trainee crucial
Word；Matching module, is configured to import at least one information of trainee key word described instruct in advance
The student's type matching model practiced carries out mating the student's type information obtaining described student；Calculate
Module, is configured to calculate described student's type information and is good at realm information with described reservation expert
Between similarity.
In certain embodiments, described matching module is configured to further: by described at least one
Student's type matching model of individual information of trainee key word importing training in advance carries out coupling and is wrapped
Containing student's type information of student's type matching degree, wherein, described student's type matching degree is used for
Characterize the accuracy determining student's type information according at least one information of trainee key word described；
First is selected according to the order that student's type matching degree is descending from each student's type information
Predetermined number student's type information forms student's type information of described student.
In certain embodiments, described expert is good at realm information signal generating unit and configures use further
In: the expert that at least one expert info key word described imports training in advance is good at field
Join model to carry out mating and obtain comprising expert and be good at the expert of field matching degree and be good at realm information,
Described expert is good at field matching degree for characterizing according at least one expert info key word described
Determine that expert is good at the accuracy of realm information；It is good at realm information according to specially from each expert
Family is good at the descending order of field matching degree and selects the second predetermined number expert to be good at field
Information forms the expert of this expert and is good at realm information.
The subscription information treating method and apparatus that the application provides, by calculating student's client
In subscription information information of trainee and reservation expert info between similarity, then when calculate gained
Similarity more than predetermined threshold time, the expert clients to reservation expert pushes of this student
Member's information；When the similarity calculating gained is less than or equal to predetermined threshold, then to student's client
In other experts pushed in candidate expert in addition to reservation expert, the similarity with information of trainee is more than
The expert info of the expert of predetermined threshold, thus be effectively utilized information of trainee and expert info it
Between similarity, improve the treatment effeciency of subscription information.
Accompanying drawing explanation
By reading retouching in detail with reference to made non-limiting example is made of the following drawings
Stating, other features, purpose and advantage will become more apparent upon:
Fig. 1 is that the application can apply to exemplary system architecture figure therein；
Fig. 2 is the flow chart of an embodiment of the subscription information processing method according to the application；
Fig. 3 a-3b is showing of an application scenarios of the subscription information processing method according to the application
It is intended to；
Fig. 4 is the flow process of another embodiment of the subscription information processing method according to the application
Figure；
Fig. 5 is the structural representation of an embodiment of the subscription information processing means according to the application
Figure；
Fig. 6 is adapted for the structure of the computer system of the server for realizing the embodiment of the present application
Schematic diagram.
Detailed description of the invention
With embodiment, the application is described in further detail below in conjunction with the accompanying drawings.It is appreciated that
, specific embodiment described herein is used only for explaining related invention, rather than to this
Bright restriction.It also should be noted that, for the ease of describe, accompanying drawing illustrate only with
About the part that invention is relevant.
It should be noted that in the case of not conflicting, the embodiment in the application and embodiment
In feature can be mutually combined.Describe this below with reference to the accompanying drawings and in conjunction with the embodiments in detail
Application.
Fig. 1 shows and the subscription information processing method of the application or subscription information can be applied to process
The exemplary system architecture 100 of the embodiment of device.
As it is shown in figure 1, system architecture 100 can include student's client device 101,102,
103, network 104, expert clients equipment 105,106 and server 107.Network 104
In order at student's client device 101,102,103, expert clients equipment 105,106 with
And the medium of communication link is provided between server 107.Network 104 can include various connection
Type, the most wired, wireless communication link or fiber optic cables etc..
Student can use student's client device 101,102,103 by network 104 and clothes
Business device 107 is mutual, to receive or to send message etc..Student's client device 101,102,103
On various client application can be installed, such as knowledge sharing reservation class application etc..
Expert can use expert clients equipment 105,106 by network 104 and server
107 is mutual, to receive or to send message etc..Can pacify on expert clients equipment 105,106
Equipped with various client application, such as knowledge sharing reservation class application etc..
Student's client device 101,102,103 and expert clients equipment 105,106 are permissible
The various electronic equipments with display screen, include but not limited to smart mobile phone, panel computer,
Pocket computer on knee and desk computer etc..
Server 107 can be to provide the server of various service, such as, set student's client
Knowledge sharing class reservation for display on 101,102,103 and expert clients 105,106 should
With providing the background server supported.Background server can to from student's client 101,102,
The data such as 103 reserve requests received are analyzed waiting and process, and by result (such as
Information of trainee) feed back to expert clients equipment 105,106, it is also possible to expert info is fed back
To student's client device 101,102,103.
It should be noted that the subscription information processing method that provided of the embodiment of the present application typically by
Server 107 performs, and correspondingly, subscription information processing means is generally positioned at server 107
In.
It should be understood that student's client device in Fig. 1, network, expert clients equipment and
The number of server is only schematically.According to realizing needs, can have any number of
Student's client device, network, expert clients equipment and server.
With continued reference to Fig. 2, it illustrates of subscription information processing method according to the application
The flow process 200 of embodiment.Described subscription information processing method, comprises the following steps:
Step 201, receives the subscription information for knowledge sharing that student's client sends.
In the present embodiment, subscription information processing method runs on electronic equipment thereon (such as
Server shown in Fig. 1) can be by wired connection mode or radio connection from student
Utilize it to carry out the terminal of knowledge sharing reservation (the such as student's client shown in Fig. 1) to receive
For the subscription information of knowledge sharing, wherein, above-mentioned subscription information includes information of trainee and reservation
Expert info, above-mentioned information of trainee includes that student's recommended information and problem describe information, above-mentioned pre-
About expert info includes expert introduction information and expert's course description information.Such as, certain student is defeated
" my student's recommended information entered graduate in Chinese Language Department of Beijing University, the tenure planning of present company A, work
Making 1 year, individual character is more shy, is not good at exchange, it is desirable to teacher can the more instruct.Appointment time
Place was as the criterion with the time of teacher, as changed the date except afternoon Wednesday At All Other Times can." and
Problem describes information, and " I wants to seek how to improve inter personal contact and how to improve communication energy to teacher
The help of power.", certain expert input expert introduction information " I is XX, incumbent money-managing consultant,
D company associating founder.The College of Physics of Beijing University graduates, and played modern drama, write novel.Finish
Enter internet industry after industry, be once one of C net 10 product managers the earliest, with C net
It is developed to industry giant from B wheel financing.It is subsequently added the Internet financial industry, successively participates in two
Venture company of family.Serving as money-managing consultant at present, it is best that target makes China's common people's Consumer's Experience
Mobile Internet tool for managing money matters.Foreground, backstage, data, strategy, conversion ... almost do
Cross all types of line of production." and expert's course description information " how to manage money matters, family income
After the life expenditure paying family, how remaining fund should manage money matters ".
It is pointed out that above-mentioned radio connection can include but not limited to 3G/4G connect,
WiFi connects, bluetooth connects, WiMAX connects, Zigbee connects, UWB (ultra wideband)
Connect and other currently known or exploitation in the future radio connection.
Step 202, calculates the similarity between information of trainee and reservation expert info.
In the present embodiment, based on the information of trainee obtained in step 201 and reservation expert info,
Above-mentioned electronic equipment (the such as server shown in Fig. 1) can calculate above-mentioned information of trainee with pre-
The about similarity between expert info.In the present embodiment, various known calculating can be used
Between text, the method for similarity calculates similarity, for example with cosine similarity (cosine
Similarity) the Text similarity computing method of algorithm, Jaccard coefficient etc.
Step 203, it is judged that whether the similarity calculating gained is more than predetermined threshold, if it is,
Forward step 204 to, if it does not, forward step 205 to.
In the present embodiment, the similarity of gained, above-mentioned electronic equipment are calculated based on step 202
May determine that whether above-mentioned similarity is more than predetermined threshold, if it is, prove in step 201
Similarity-rough set between the information of trainee and the reservation expert info that receive is high, forwards step to
204 by examination & verification；If it is not, then prove information of trainee and the reservation received in step 201
Similarity-rough set between expert info is low, and the two is uncorrelated, then forward step 205 to, continues
Other more relevant experts are recommended to student.
Step 204, the expert clients to the reservation expert indicated by reservation expert info pushes
Information of trainee is accepted by expert clients for preengaging expert or refuses the pre-of student's client
About.
In the present embodiment, the similarity between information of trainee and reservation expert info is more than pre-
In the case of determining threshold value, it was demonstrated that the information of trainee received in step 201 and reservation expert info
Between similarity-rough set high, the most above-mentioned electronic equipment can be to indicated by reservation expert info
The expert clients of reservation expert pushes above-mentioned information of trainee, for reservation expert by expert visitor
Family terminates by or refuses above-mentioned student's client and preengage.Here, reservation expert is receiving
According to the student's recommended information in information of trainee and problem, letter can be described after above-mentioned information of trainee
Breath, uses expert clients to accept or refuses the reservation of above-mentioned student's client.
Step 205, to each expert in other experts in addition to reservation expert in candidate expert,
Calculate the similarity between information of trainee and the expert info of this expert.
In the present embodiment, the similarity between information of trainee and reservation expert info is less than
In the case of predetermined threshold, it was demonstrated that the information of trainee received in step 201 and reservation expert
Between information, similarity-rough set is low, needs to recommend other experts, the most above-mentioned electricity to above-mentioned student
Subset can be to each expert in other experts in addition to reservation expert in candidate expert, meter
Count the similarity stating between information of trainee and the expert info of this expert in.Here similarity is calculated
Method can be and the identical method used when calculating similarity in step 202.
Step 206, chooses the similarity candidate expert more than predetermined threshold as recommending expert.
In the present embodiment, the similarity that above-mentioned electronic equipment can calculate based on step 205,
The similarity candidate expert more than predetermined threshold is chosen as recommendation expert, use from candidate expert
Above-mentioned student is given in subsequent recommendation.
Step 207, recommends the expert info of expert to student's client push.
In the present embodiment, the similarity that step 206 selects is more than the candidate expert of predetermined threshold
Being the expert that expert info is high with the similarity-rough set of information of trainee, the most above-mentioned electronic equipment can
With by these recommend experts expert info be pushed to student's client, can improve expert info with
Dependency between information of trainee, helps student to be quickly found out oneself expert applicable.
It is the subscription information processing method according to the present embodiment with continued reference to Fig. 3 a-3b, Fig. 3 a-3b
A schematic diagram of application scenarios.In the application scenarios of Fig. 3 a-3b, first student inputs
Student's recommended information (as shown in icon 301 in Fig. 3 a) and problem describe information (such as Fig. 3 a
Shown in middle icon 302), then browsing to the expert info (icon in such as Fig. 3 b of king expert
303 and icon 304 shown in) time, initiated the reserve requests to king expert (as in Fig. 3 b scheme
Shown in mark 305)；Afterwards, server can with backstage obtain above-mentioned reserve requests information of trainee and
Expert info；Then, above-mentioned server calculates the phase between above-mentioned information of trainee and expert info
Like degree；Next, it is determined that above-mentioned similarity is more than predetermined threshold；Finally, above-mentioned server is permissible
Above-mentioned information of trainee is sent to the expert clients of above-mentioned king expert.
The method that above-described embodiment of the application provides shares of reserve requests by calculation knowledge
Similarity between member's information and reservation expert info, improves the treatment effeciency of subscription information.
With further reference to Fig. 4, it illustrates another embodiment of subscription information processing method
Flow process 400.The flow process 400 of this subscription information processing method, comprises the following steps:
Step 401, expert introduction information and expert's course description information to expert are added up
Analyze and/or semantic analysis, obtain at least one expert info key word.
In the present embodiment, subscription information processing method runs on electronic equipment thereon (such as
Server shown in Fig. 1) can be to the expert introduction information received from expert clients with special
Family's course description information carries out statistical analysis and/or semantic analysis, obtains at least one expert info
Key word.In the present embodiment, to above-mentioned expert introduction information and expert's course description information
Analysis mode can be to be statistical analysis mode.For example, it is possible to present in above-mentioned information each
The frequency of occurrences of word is added up and sorts, and afterwards, then it is forward to choose frequency of occurrences sequence
One or more words are as expert info key word.Or, to above-mentioned expert introduction information and
The analysis mode of expert's course description information can also is that semantic analysis mode.As example, can
Expert introduction information and expert's course description information to be carried out the process such as full cutting method, interior
Hold and be divided into word；Obtained word is carried out importance calculating again (for example with the literary composition of word frequency-inversely
Part frequency approach (Term Frequency-Inverse Document Frequency, TF-IDF)),
The result calculated based on importance obtains key word.It will be appreciated by persons skilled in the art that
Can also comprehensively use statistical analysis and semantic analysis both means crucial to obtain expert info
Word.
Such as, to expert introduction information, " I is XX, incumbent money-managing consultant, and D company combines
Founder.The College of Physics of Beijing University graduates, and played modern drama, write novel.Enter mutually after graduation
Networking industry, was once one of C net 10 product managers the earliest, with C net from B wheel financing
It is developed to industry giant.It is subsequently added the Internet financial industry, successively participates in Liang Jia venture company.
Serving as money-managing consultant at present, target makes the mobile Internet that China's common people's Consumer's Experience is best
Tool for managing money matters.Foreground, backstage, data, strategy, conversion ... almost did all types of
The line of production." and expert course description information " how managing money matters, family income is paying family
After life expenditure, how remaining fund should manage money matters " carry out statistical analysis and/or semantic analysis
After obtain at least one expert info key word " move ", " financial ", " financing ", " product "
" the Internet ".
Step 402, is good at the expert that at least one expert info key word imports training in advance
Field Matching Model carries out coupling and obtains expert and be good at realm information.
In the present embodiment, above-mentioned electronic equipment step 401 can be obtained at least one is special
Family's information key imports the expert of training in advance and is good at field Matching Model；Expert is good at field
Matching Model, according to the good corresponding relation of training in advance, finds and at least one expert info is crucial
The expert that word is corresponding is good at realm information and is good at realm information as the expert of above-mentioned expert.Such as,
At least one expert info key word " is moved ", " financial ", " financing ", " product " and " mutually
Networking " import expert and be good at field Matching Model and carry out coupling and obtain expert and be good at realm information " mutually
Networking " and " financing ".
In some optional implementations of the present embodiment, above-mentioned electronic equipment can first by
At least one expert info key word above-mentioned imports the expert of training in advance and is good at field Matching Model
Mate, obtain comprising expert and be good at the expert of field matching degree and be good at realm information, wherein,
Expert is good at field matching degree and determines according at least one expert info key word above-mentioned for characterizing
Expert is good at the accuracy of realm information, and expert is good at field matching degree can use various ways table
Show, include but not limited to the form etc. of percents or numerical values recited；Then from each expert
It is good in realm information and is good at the descending order of field matching degree according to expert and selects second pre-
Fixed number mesh expert is good at the expert of the realm information above-mentioned expert of composition and is good at realm information.Wherein,
The concrete value of the second predetermined number can be configured according to actual needs.
Step 403, receives the subscription information for knowledge sharing that student's client sends.
In the present embodiment, above-mentioned electronic equipment (the such as server shown in Fig. 1) can lead to
Crossing wired connection mode or radio connection utilizes it to carry out knowledge sharing reservation from student
Terminal (the such as student's client shown in Fig. 1) receives the subscription information for knowledge sharing,
Wherein, above-mentioned subscription information includes information of trainee and reservation expert info, above-mentioned information of trainee bag
Including student's recommended information and problem describes information, above-mentioned reservation expert info includes that expert introduction is believed
Breath and expert's course description information.
Step 404, carries out statistical analysis and/or semantic analysis to information of trainee, extracts at least one
Individual information of trainee key word.
In the present embodiment, above-mentioned electronic equipment can be to the student received from student's client
Information carries out statistical analysis and/or semantic analysis, obtains at least one information of trainee key word.Example
As, for student's recommended information " I Chinese Language Department of Beijing University graduate, present company A tenure planning,
Working 1 year, individual character is more shy, is not good at exchange, it is desirable to teacher can the more instruct.During meet
Between place be as the criterion with the time of teacher, as changed the date except afternoon Wednesday At All Other Times can.”
" I wants to seek how to improve inter personal contact and how to improve communication to teacher to describe information with problem
The help of ability." after statistical analysis and/or semantic analysis, obtain information of trainee key word:
" advertisement ", " planning " and " Chinese Language Department ".
Step 405, imports student's type of training in advance by least one information of trainee key word
Matching Model carries out mating the student's type information obtaining student.
In the present embodiment, at least one that step 404 can be obtained by above-mentioned electronic equipment
Member's information key imports student's type matching model of training in advance；Student's type matching model
According to the corresponding relation that training in advance is good, find corresponding with at least one information of trainee key word
Student's type information is as student's type information of above-mentioned student.Such as, information of trainee is crucial
Word: " advertisement ", " planning " and " Chinese Language Department " imports student's type matching model and carry out mating
Student's type information to student is " broadcasting media ".
In some optional implementations of the present embodiment, first above-mentioned electronic equipment can also be
Student's type matching model that at least one information of trainee key word above-mentioned imports training in advance is entered
Row coupling, obtains comprising student's type information of student's type matching degree, wherein, student's type
Matching degree determines student's type information for characterizing according at least one information of trainee key word above-mentioned
Accuracy, student's type matching degree can represent by various ways, includes but not limited to percentage
Than form or the form etc. of numerical values recited；Then according to student's class from each student's type information
The descending order of type matching degree selects first predetermined number student's type information composition above-mentioned
Student's type information of student.Wherein, the concrete value of the first predetermined number can be according to reality
Needs are configured.
Step 406, calculates the phase that student's type information is good between realm information with reservation expert
Like degree.
In the present embodiment, based on the student's type information obtained in step 405 and step 403
In receive reservation expert be good at realm information, above-mentioned electronic equipment is (such as shown in Fig. 1
Server) student's type information and reservation being good between realm information of expert can be calculated
Similarity.In the present embodiment, the side of similarity between various known calculating text can be used
Method calculates similarity, for example with cosine similarity (cosine similarity) algorithm, Jaccard
The Text similarity computing method of coefficient etc calculates similarity.With Jaccard coefficient method it is
Example, student's type information and the similarity being good between realm information=student's type of reservation expert
Information and the number/student's type information being good between realm information total word of reservation expert
With reservation expert be good at realm information together with the number of word that includes.
Step 407, it is judged that whether the similarity calculating gained is more than predetermined threshold, if it is,
Forward step 408 to, if it does not, forward step 409 to.
In the present embodiment, the similarity of gained, above-mentioned electronic equipment are calculated based on step 406
May determine that whether above-mentioned similarity is more than predetermined threshold, if it is, prove in step 403
Similarity-rough set between the information of trainee and the reservation expert info that receive is high, forwards step to
408 by examination & verification；If it is not, then prove information of trainee and the reservation received in step 403
Similarity-rough set between expert info is low, and the two is uncorrelated, then forward step 409 to, continues
Other more relevant experts are recommended to student.
Step 408, the expert clients to the reservation expert indicated by reservation expert info pushes
Information of trainee is accepted by expert clients for preengaging expert or refuses the pre-of student's client
About.
In the present embodiment, the similarity between information of trainee and reservation expert info is more than pre-
In the case of determining threshold value, it was demonstrated that the information of trainee received in step 403 and reservation expert info
Between similarity-rough set high, the most above-mentioned electronic equipment can be to indicated by reservation expert info
The expert clients of reservation expert pushes above-mentioned information of trainee, for reservation expert by expert visitor
Family terminates by or refuses student's client and preengage.Here, reservation expert receive above-mentioned
Information can be described according to the student's recommended information in information of trainee and problem after information of trainee, make
Accept or refuse the reservation of this student's client by expert clients.
Step 409, to each expert in other experts in addition to reservation expert in candidate expert,
Calculate the similarity between information of trainee and the expert info of this expert.
In the present embodiment, the similarity between information of trainee and reservation expert info is less than
In the case of predetermined threshold, it was demonstrated that the information of trainee received in step 403 and reservation expert
Between information, similarity-rough set is low, needs to recommend other experts, the most above-mentioned electricity to above-mentioned student
Subset can be to each expert in other experts in addition to reservation expert in candidate expert, meter
Calculate the similarity between information of trainee and the expert info of this expert.Here the side of similarity is calculated
Method can be and the identical method used when calculating similarity in step 406.
Step 410, chooses the similarity candidate expert more than predetermined threshold as recommending expert.
In the present embodiment, the similarity that above-mentioned electronic equipment can calculate based on step 409,
The similarity candidate expert more than predetermined threshold is chosen as recommendation expert, use from candidate expert
Above-mentioned student is given in subsequent recommendation.
Step 411, recommends the expert info of expert to student's client push.
In the present embodiment, the similarity that step 410 selects is more than the candidate expert of predetermined threshold
Being the expert that expert info is high with the similarity-rough set of information of trainee, the most above-mentioned electronic equipment can
With by these recommend experts expert info be pushed to student's client, can improve expert info with
Dependency between information of trainee, helps student to be quickly found out oneself expert applicable.
Figure 4, it is seen that compared with the embodiment that Fig. 2 is corresponding, pre-in the present embodiment
The about flow process 400 of information processing method has had more the step being good at realm information generating expert
401 and step 402, and step 404-406 highlights and is analyzed extracting also to information of trainee
Obtain student's type information after coupling, finally calculate student's type information and be good at neck with reservation expert
Similarity between domain information.Thus, the scheme that the present embodiment describes can introduce student's type
Information and reservation expert are good at realm information, thus realize more effective subscription information and process.
With further reference to Fig. 5, as to the realization of method shown in above-mentioned each figure, the application provides
A kind of embodiment of subscription information processing means, this device embodiment with shown in Fig. 2
Embodiment of the method is corresponding, and this device specifically can apply in various electronic equipment.
As it is shown in figure 5, the subscription information processing means 500 described in the present embodiment includes: receive
Unit 501, computing unit the 502, first push unit 503 and the second push unit 504.Its
In, receive unit 501, be configured to receive that student's client sends for knowledge sharing
Subscription information, wherein, above-mentioned subscription information includes information of trainee and reservation expert info, above-mentioned
Information of trainee includes that student's recommended information and problem describe information, and above-mentioned reservation expert info includes
Expert introduction information and expert's course description information；Computing unit 502, is configured to calculate
State the similarity between information of trainee and above-mentioned reservation expert info；First push unit 503,
It is configured to the similarity in response to calculating gained and is more than predetermined threshold, believe to above-mentioned reservation expert
It is special for above-mentioned reservation that the expert clients of the reservation expert indicated by breath pushes above-mentioned information of trainee
Family is accepted by above-mentioned expert clients or is refused the reservation of above-mentioned student's client；Second pushes away
Send unit 504, be configured in response to calculating the similarity of gained less than or equal to above-mentioned predetermined threshold
Value, to each expert in other experts in addition to above-mentioned reservation expert in candidate expert, calculates
Similarity between above-mentioned information of trainee and the expert info of this expert；Choose similarity more than upper
State the candidate expert of predetermined threshold as recommending expert；Push away to above-mentioned student's client push is above-mentioned
Recommend the expert info of expert.
In the present embodiment, the reception unit 501 of subscription information processing means 500, calculating list
Unit's the 502, first push unit 503 and specifically the processing and bring of the second push unit 504
Technique effect can be respectively with reference to step 201, step 202, step in Fig. 2 correspondence embodiment
203 and the related description of implementation of step 204, do not repeat them here.
In some optional implementations of the present embodiment, the subscription information that the present embodiment provides
Processing means 500 can also include that expert is good at realm information signal generating unit (not shown),
It is configured to: expert introduction information and expert's course description information to expert carry out statistical analysis
And/or semantic analysis, obtain at least one expert info key word；By at least one expert above-mentioned
Information key imports the expert of training in advance and is good at field Matching Model and carries out coupling and obtain expert
It is good at realm information.Expert is good at realm information signal generating unit concrete and processes and brought
Technique effect refers to the implementation of step 401 and step 402 in Fig. 4 correspondence embodiment
Related description, does not repeats them here.
In some optional implementations of the present embodiment, the subscription information that the present embodiment provides
The computing unit 502 of processing means 500 may include that extraction module (not shown), joins
Put for above-mentioned information of trainee is carried out statistical analysis and/or semantic analysis, extract at least one and learn
Member's information key；Matching module (not shown), is configured at least one above-mentioned
Student's type matching model of member's information key importing training in advance carries out coupling and obtains above-mentioned
Student's type information of member；Computing module (not shown), is configured to calculate above-mentioned student
The similarity that type information and above-mentioned reservation expert are good between realm information.Extraction module,
The concrete technique effect processing and being brought joining module and computing module can be respectively with reference to Fig. 4
The related description of the implementation of step 404, step 405 and step 406 in corresponding embodiment,
Do not repeat them here.
In some optional implementations of the present embodiment, the subscription information that the present embodiment provides
The matching module of the computing unit 502 of processing means 500 can be configured to further: by upper
State at least one information of trainee key word to import student's type matching model of training in advance and carry out
Join the student's type information obtaining comprising student's type matching degree, wherein, above-mentioned student's type
Degree of joining determines student's type information for characterizing according at least one information of trainee key word above-mentioned
Accuracy；According to the order that student's type matching degree is descending from each student's type information
First predetermined number student's type information is selected to form student's type information of above-mentioned student.?
The concrete technique effect processing and being brought joining module refers to walk in Fig. 4 correspondence embodiment
The related description of the implementation of rapid 405, does not repeats them here.
In some optional implementations of the present embodiment, the subscription information that the present embodiment provides
The expert of processing means 500 is good at realm information signal generating unit and can be configured to further: will
At least one expert info key word above-mentioned imports the expert of training in advance and is good at field Matching Model
Carry out mating and obtain comprising expert and be good at the expert of field matching degree and be good at realm information, above-mentioned specially
Family is good at field matching degree and determines specially according at least one expert info key word above-mentioned for characterizing
The accuracy of realm information is good at by family；It is good at realm information from each expert and is good at according to expert
Matching degree descending order in field selects the second predetermined number expert to be good at realm information group
The expert becoming above-mentioned expert is good at realm information.Expert is good at the concrete of realm information signal generating unit
The technique effect processed and brought refers to step 401 and step in Fig. 4 correspondence embodiment
The related description of the implementation of 402, does not repeats them here.
Below with reference to Fig. 6, it illustrates the server that is suitable to for realizing the embodiment of the present application
The structural representation of computer system 600.
As shown in Figure 6, computer system 600 includes CPU (CPU) 601, its
Can be according to the program being stored in read only memory (ROM) 602 or from storage part 606
It is loaded into the program in random access storage device (RAM) 603 and performs various suitable action
And process.In RAM 603, also storage has system 600 to operate required various program sums
According to.CPU 601, ROM 602 and RAM 603 are connected with each other by bus 604.Input
/ output (I/O) interface 605 is also connected to bus 604.
It is connected to I/O interface 605: include the storage part 606 of hard disk etc. with lower component；And
Communications portion 607 including the NIC of such as LAN card, modem etc..Communication
Part 607 performs communication process via the network of such as the Internet.Driver 608 is also according to needing
I/O interface 605 to be connected to.Detachable media 609, such as disk, CD, magneto-optic disk,
Semiconductor memory etc., is arranged in driver 608, in order to read from it as required
The computer program gone out is mounted into storage part 606 as required.
Especially, according to embodiment of the disclosure, the process described above with reference to flow chart is permissible
It is implemented as computer software programs.Such as, embodiment of the disclosure and include a kind of computer journey
Sequence product, it includes the computer program being tangibly embodied on machine readable media, described meter
Calculation machine program comprises the program code for performing the method shown in flow chart.In such enforcement
In example, this computer program can be downloaded and installed from network by communications portion 607,
And/or be mounted from detachable media 609.At this computer program by CPU
(CPU), during 601 execution, the above-mentioned functions limited in the present processes is performed.
Flow chart in accompanying drawing and block diagram, it is illustrated that according to the various embodiment of the application system,
Architectural framework in the cards, function and the operation of method and computer program product.This point
On, each square frame in flow chart or block diagram can represent a module, program segment or code
A part, a part for described module, program segment or code comprise one or more for
Realize the executable instruction of the logic function of regulation.It should also be noted that at some as replacement
In realization, the function marked in square frame can also be sent out to be different from the order marked in accompanying drawing
Raw.Such as, two square frames succeedingly represented can essentially perform substantially in parallel, they
Sometimes can also perform in the opposite order, this is depending on involved function.It is also noted that
It is, the square frame in each square frame in block diagram and/or flow chart and block diagram and/or flow chart
Combination, can realize by the special hardware based system of the function or operation that perform regulation,
Or can realize with the combination of specialized hardware with computer instruction.
Being described in the embodiment of the present application involved unit can be real by the way of software
Existing, it is also possible to realize by the way of hardware.Described unit can also be arranged on process
In device, for example, it is possible to be described as: a kind of processor include receiving unit, computing unit, the
One push unit and the second push unit.Wherein, the title of these unit is the most also
Do not constitute the restriction to this unit itself, such as, receive unit and be also described as " receiving
The unit of subscription information ".
As on the other hand, present invention also provides a kind of nonvolatile computer storage media,
This nonvolatile computer storage media can be described in above-described embodiment included in device
Nonvolatile computer storage media；Can also be individualism, be unkitted allocate in terminal non-
Volatile computer storage medium.Above-mentioned nonvolatile computer storage media storage have one or
The multiple program of person, when one or more program is performed by an equipment so that described
Equipment: receive the subscription information for knowledge sharing that student's client sends, wherein, above-mentioned
Subscription information includes information of trainee and reservation expert info, and above-mentioned information of trainee includes that student introduces
Information and problem describe information, and above-mentioned reservation expert info includes expert introduction information and expert's class
Journey describes information；Calculate the similarity between above-mentioned information of trainee and above-mentioned reservation expert info；
It is more than predetermined threshold, to indicated by above-mentioned reservation expert info in response to the similarity calculating gained
The expert clients of reservation expert push above-mentioned information of trainee for above-mentioned reservation expert by upper
State the reservation that expert clients accepts or refuses above-mentioned student's client；In response to calculating gained
Similarity less than or equal to above-mentioned predetermined threshold, in candidate expert in addition to above-mentioned reservation expert
Each expert in other experts, calculates between above-mentioned information of trainee and the expert info of this expert
Similarity；Choose the similarity candidate expert more than above-mentioned predetermined threshold as recommending expert；
Expert info to above-mentioned student's client push above-mentioned recommendation expert.
Above description is only the preferred embodiment of the application and saying institute's application technology principle
Bright.It will be appreciated by those skilled in the art that invention scope involved in the application, do not limit
In the technical scheme of the particular combination of above-mentioned technical characteristic, also should contain simultaneously without departing from
In the case of described inventive concept, above-mentioned technical characteristic or its equivalent feature carry out combination in any
And other technical scheme formed.Such as features described above and (but not limited to) disclosed herein
The technical characteristic with similar functions is replaced mutually and the technical scheme that formed.
Claims (10)
1. a subscription information processing method, it is characterised in that described method includes:
Receive the subscription information for knowledge sharing that student's client sends, wherein, described pre-
About information includes information of trainee and reservation expert info, and described information of trainee includes student's reference
Breath and problem describe information, and described reservation expert info includes expert introduction information and expert's course
Description information；
Calculate the similarity between described information of trainee and described reservation expert info；
It is more than predetermined threshold, to described reservation expert info institute in response to the similarity calculating gained
The expert clients of the reservation expert of instruction pushes described information of trainee and leads to for described reservation expert
Cross the reservation that described expert clients accepts or refuses described student's client；
It is less than or equal to described predetermined threshold, in candidate expert in response to the similarity calculating gained
The each expert in other experts in addition to described reservation expert, calculates described information of trainee and is somebody's turn to do
Similarity between the expert info of expert；Choose the similarity candidate more than described predetermined threshold
Expert is as recommending expert；To the expert info recommending expert described in described student's client push.
Method the most according to claim 1, it is characterised in that described method also includes:
The expert introduction information of expert and expert's course description information are carried out statistical analysis and/or
Semantic analysis, obtains at least one expert info key word；
The expert that at least one expert info key word described imports training in advance is good at field
Join model to carry out mating and obtain the expert of described expert and be good at realm information.
Method the most according to claim 2, it is characterised in that the described student of described calculating
Similarity between information and described reservation expert info, including:
Described information of trainee is carried out statistical analysis and/or semantic analysis, extracts at least one student
Information key；
At least one information of trainee key word described is imported student's type matching mould of training in advance
Type carries out mating the student's type information obtaining described student；
Calculate described student's type information similar to what described reservation expert was good between realm information
Degree.
Method the most according to claim 3, it is characterised in that described by described at least one
Student's type matching model of individual information of trainee key word importing training in advance carries out coupling and obtains institute
State student's type information of student, including:
At least one information of trainee key word described is imported student's type matching mould of training in advance
Type carries out mating the student's type information obtaining comprising student's type matching degree, wherein, described
Member's type matching degree is used for characterizing and determines student's class according at least one information of trainee key word described
The accuracy of type information；
Select according to the order that student's type matching degree is descending from each student's type information
First predetermined number student's type information forms student's type information of described student.
Method the most according to claim 2, it is characterised in that described by described at least one
The expert of individual expert info key word importing training in advance is good at field Matching Model and carries out mating
Expert to this expert is good at realm information, including:
The expert that at least one expert info key word described imports training in advance is good at field
Join model to carry out mating and obtain comprising expert and be good at the expert of field matching degree and be good at realm information,
Described expert is good at field matching degree for characterizing according at least one expert info key word described
Determine that expert is good at the accuracy of realm information；
It is good at realm information from each expert that to be good at field matching degree according to expert descending
Order selects the second predetermined number expert to be good at realm information to form the expert of this expert and be good at neck
Domain information.
6. a subscription information processing means, it is characterised in that described device includes:
Receive unit, be configured to receive the reservation for knowledge sharing that student's client sends
Information, wherein, described subscription information includes information of trainee and reservation expert info, described student
Information includes that student's recommended information and problem describe information, and described reservation expert info includes expert
Recommended information and expert's course description information；
Computing unit, is configured to calculate between described information of trainee and described reservation expert info
Similarity；
First push unit, is configured to the similarity in response to calculating gained and is more than predetermined threshold,
Expert clients to the reservation expert indicated by described reservation expert info pushes described student letter
Cease and accept or refuse described student client for described reservation expert by described expert clients
The reservation of end；
Second push unit, is configured in response to calculating the similarity of gained less than or equal to described
Predetermined threshold, to each expert in other experts in addition to described reservation expert in candidate expert,
Calculate the similarity between described information of trainee and the expert info of this expert；Choose similarity big
In described predetermined threshold candidate expert as recommend expert；To described student's client push institute
State the expert info recommending expert.
Device the most according to claim 6, it is characterised in that described device also includes specially
Realm information signal generating unit is good at by family, is configured to:
The expert introduction information of expert and expert's course description information are carried out statistical analysis and/or
Semantic analysis, obtains at least one expert info key word；
The expert that at least one expert info key word described imports training in advance is good at field
Join model to carry out mating and obtain the expert of described expert and be good at realm information.
Device the most according to claim 7, it is characterised in that described computing unit includes:
Extraction module, is configured to described information of trainee carries out statistical analysis and/or semanteme point
Analysis, extracts at least one information of trainee key word；
Matching module, is configured to import at least one information of trainee key word described instruct in advance
The student's type matching model practiced carries out mating the student's type information obtaining described student；
Computing module, is configured to calculate described student's type information and is good at described reservation expert
Similarity between realm information.
Device the most according to claim 8, it is characterised in that described matching module enters one
Step is configured to:
At least one information of trainee key word described is imported student's type matching mould of training in advance
Type carries out mating the student's type information obtaining comprising student's type matching degree, wherein, described
Member's type matching degree is used for characterizing and determines student's class according at least one information of trainee key word described
The accuracy of type information；
Select according to the order that student's type matching degree is descending from each student's type information
First predetermined number student's type information forms student's type information of described student.
Device the most according to claim 7, it is characterised in that described expert is good at neck
Domain information signal generating unit is configured to further:
The expert that at least one expert info key word described imports training in advance is good at field
Join model to carry out mating and obtain comprising expert and be good at the expert of field matching degree and be good at realm information,
Described expert is good at field matching degree for characterizing according at least one expert info key word described
Determine that expert is good at the accuracy of realm information；
It is good at realm information from each expert that to be good at field matching degree according to expert descending
Order selects the second predetermined number expert to be good at realm information to form the expert of this expert and be good at neck
Domain information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610301168.5A CN105955961B (en) | 2016-05-09 | 2016-05-09 | Reservation information treating method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610301168.5A CN105955961B (en) | 2016-05-09 | 2016-05-09 | Reservation information treating method and apparatus |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105955961A true CN105955961A (en) | 2016-09-21 |
| CN105955961B CN105955961B (en) | 2019-03-12 |
Family
ID=56914154
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610301168.5A Active CN105955961B (en) | 2016-05-09 | 2016-05-09 | Reservation information treating method and apparatus |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105955961B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815747A (en) * | 2017-01-18 | 2017-06-09 | 百度在线网络技术（北京）有限公司 | Method and apparatus for sending information |
| CN107943931A (en) * | 2017-11-22 | 2018-04-20 | 上海心灵伙伴览育信息技术有限公司 | Visitor and the matching process and system of consultant |
| CN109272130A (en) * | 2018-09-21 | 2019-01-25 | 广州神马移动信息科技有限公司 | Question and answer exchange method and its device |
| CN109460504A (en) * | 2018-09-21 | 2019-03-12 | 广州神马移动信息科技有限公司 | The answer main body recommended method and its device, electronic equipment, computer-readable medium of answer are reserved in Knowledge Community |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081617A (en) * | 2009-11-26 | 2011-06-01 | 英业达股份有限公司 | Online consultation method and system |
| KR20120087412A (en) * | 2011-01-28 | 2012-08-07 | 주식회사 유프레스토 | optimal specialist consulting system using an online and controlling method therefore |
| CN103279884A (en) * | 2013-06-19 | 2013-09-04 | 沈文策 | Consultation service providing method and system |
| CN103401873A (en) * | 2013-08-07 | 2013-11-20 | 上海法度网络科技有限公司 | System and method for realizing stream media expert service based on network |
-
2016
- 2016-05-09 CN CN201610301168.5A patent/CN105955961B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081617A (en) * | 2009-11-26 | 2011-06-01 | 英业达股份有限公司 | Online consultation method and system |
| KR20120087412A (en) * | 2011-01-28 | 2012-08-07 | 주식회사 유프레스토 | optimal specialist consulting system using an online and controlling method therefore |
| CN103279884A (en) * | 2013-06-19 | 2013-09-04 | 沈文策 | Consultation service providing method and system |
| CN103401873A (en) * | 2013-08-07 | 2013-11-20 | 上海法度网络科技有限公司 | System and method for realizing stream media expert service based on network |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815747A (en) * | 2017-01-18 | 2017-06-09 | 百度在线网络技术（北京）有限公司 | Method and apparatus for sending information |
| CN107943931A (en) * | 2017-11-22 | 2018-04-20 | 上海心灵伙伴览育信息技术有限公司 | Visitor and the matching process and system of consultant |
| CN109272130A (en) * | 2018-09-21 | 2019-01-25 | 广州神马移动信息科技有限公司 | Question and answer exchange method and its device |
| CN109460504A (en) * | 2018-09-21 | 2019-03-12 | 广州神马移动信息科技有限公司 | The answer main body recommended method and its device, electronic equipment, computer-readable medium of answer are reserved in Knowledge Community |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105955961B (en) | 2019-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7464098B2 (en) | Electronic conference system | |
| Zhang et al. | User community discovery from multi-relational networks | |
| DE112018002952T5 (en) | Data comparison based on computer analysis of data | |
| CN106227786A (en) | Method and apparatus for pushed information | |
| CN107480858A (en) | A kind of Aided intelligent decision-making and method based on the analysis of stock big data | |
| US20190355080A9 (en) | Method and system for curriculum management services | |
| CN103425678B (en) | The system of selection of the recommendation information of a kind of business object and device | |
| CN105955961A (en) | Reservation information processing method and apparatus | |
| CN105701088A (en) | Method and device for switching machine conversation to artificial conversation | |
| CN110020022B (en) | Data processing method, device, equipment and readable storage medium | |
| CN111581966A (en) | Context feature fusion aspect level emotion classification method and device | |
| US9501580B2 (en) | Method and apparatus for automated selection of interesting content for presentation to first time visitors of a website | |
| Jain et al. | Emerging dimensions of blockchain application in tourism and hospitality sector: a systematic literature review | |
| CN107016132A (en) | Online question bank quality improving method and system and terminal equipment | |
| CN107392982A (en) | Photographing On-line method, apparatus and system | |
| CN107193974A (en) | Localized information based on artificial intelligence determines method and apparatus | |
| Solis et al. | Modeling multileader–follower noncooperative Stackelberg games | |
| CN105912575A (en) | Text information pushing method and text information pushing device | |
| CN108595403A (en) | Processing method, computing device for assisting writing and storage medium | |
| Denk | Comparative multilevel analysis: proposal for a methodology | |
| CN104077288B (en) | Web page contents recommend method and web page contents recommendation apparatus | |
| Harguem et al. | Machine Learning Based Prediction of Stock Exchange on NASDAQ 100: A Twitter Mining Approach | |
| Valvi et al. | Online and mobile customer behaviour: a critical evaluation of Grounded Theory studies | |
| Nguyen et al. | Towards a holistic sustainability evaluation for transport alternatives | |
| CN105931032A (en) | Intelligent household platform, method and apparatus for pushing information applied to platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |