CN108364198A - A kind of online motivational techniques of mobile crowdsourcing based on social networks - Google Patents
A kind of online motivational techniques of mobile crowdsourcing based on social networks Download PDFInfo
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Abstract
The online motivational techniques of mobile crowdsourcing based on social networks that the invention discloses a kind of, include the following steps：1, to the task T that master budget is B, crowdsourcing platform γ user of selection from registration user's set J constitutes agent list A, and calculates and each act on behalf of AkPay the budget of user in its social networks；2, task T is decomposed into multiple subtasks, constitutes set of tasks Γ；And at the beginning of constraining each subtask and the end time；Set of tasks Γ is distributed to each agency in agent list A by crowdsourcing platform；3, after the arbitrary agency in agent list A reaches the standard grade, A is each acted on behalf ofkThe subtask set Γ that current time is not yet completedkIt is sent to AkSocial networks, AkOnline user in social networks competes subtask by mode of bid, and calculates remuneration for bid victor；4, target subtask during victor executes, is sent to crowdsourcing platform by implementing result and obtains remuneration.This method is suitable for extensive crowdsourcing task.
Description
Technical field
The invention belongs to crowdsourcing software fields in internet, and in particular to a kind of mobile crowdsourcing based on social networks is online
Motivational techniques.
Background technology
Crowdsourcing refers to the task that a company or mechanism execute past employee, the outsourcing in the form of freedom is voluntary
To the way of unspecific public network.The advantage of crowdsourcing is that, using individual, team, the wisdom of community goes to complete task.Closely
Several years, crowdsourcing was widely used in many fields, such as video analysis, intelligent city, machine learning, information retrieval, social networks
Equal fields.With popularizing for smart mobile phone, movement crowdsourcing, which is increasingly becoming, meets a kind of effective of a wide range of sensing mission requirements
Method.
But these applications at present assume that enough participants go to execute crowdsourcing task, this is often unrealistic, especially
It completes some with extensive crowdsourcing task on the crowdsourcing platform of demand.
Invention content
Goal of the invention：For problems of the prior art, the present invention provides a kind of movements based on social networks
The online motivational techniques of crowdsourcing are suitable for extensive crowdsourcing task.
Technical solution：The present invention adopts the following technical scheme that：
A kind of online motivational techniques of mobile crowdsourcing based on social networks, include the following steps：
(1) to the task T that master budget is B, crowdsourcing platform γ user of selection from registration user's set J constitutes agency's collection
A is closed, wherein positive integer γ is constant；And it calculates and each acts on behalf of AkPay the budget B of user in its social networksk；
(2) task T is decomposed into multiple subtasks, constitutes set of tasks Γ={ τ1,τ2,...,τm}；Each subtask τp
It is 0 at the beginning of ∈ Γ, end time ep, epFor the integral multiple of the unit time 1 of discretization, p=1..m；Crowdsourcing platform
Each agency set of tasks Γ being distributed in agent list A；
(3) after the arbitrary agency in agent list A reaches the standard grade, A is each acted on behalf ofkThe subtask that current time can be not yet completed
Set ΓkIt is sent to AkSocial networks, AkOnline user in social networks competes subtask by mode of bid；And to throw
It marks victor and calculates remuneration；K=1.. γ；
(4) target subtask during victor executes, is sent to crowdsourcing platform by implementing result and obtains remuneration.
Crowdsourcing platform determines that the specific steps of agent list A include：
(101) initialization agent list A is empty set, s=γ, traversal registration user set J, according to going through for registration user j
History line duration section HjTimes of each registration user j to online task time section is calculated with online task time section H
Covering, wherein HjIt can be obtained according to the history online record of registration user, H=[0, max { e1,e2,...,em], registration user j
H is covered as to the time in online task time sectionjWith the time of coincidence section of H；j∈J；
(102) the selection arg max from registration user's set Jh∈J\A(Hh∩ H) it is user j, wherein operation ∩ expressions ask two
The time of coincidence section of a time interval；
(103) pass through influence power calculation formulaInfluence powers of the user j in its social networks is calculated,
Middle lnfi jThe possibility of task is participated in for social user i in the social networks of user j, calculation formula isJac(Γj, i) and it is that user j reaches the standard grade the unfinished subtask set Γ of fashionk
Outstanding person between social activity user i set of tasks interested in its social networks blocks German number, and calculation formula isWherein IiIt, can be from social network user for the interested subtask set of social user i
It is extracted in Profile, ImaxFor the biggest impact factor, it is greater than 1 constant；SNjFor social user in user's j social networks
Set；
(104) if Hj∩H>0 and ln fj/|Hj|>δ, wherein δ are constant, then by user j addition agent list A, and from
Remove in J in user j, H and removes Hj, the value of s subtracts 1；
(105) whenS ≠ 0, andWhen, step 101-104 is repeated, until constituting comprising γ agency
Agent list A.
(106) A is acted on behalf of to each of agent list Ak, calculate it and pay the budget B of user in its social networksk, Bk's
Calculation formula is Bk=(lnfk/∑q∈Alnfq)B。
A in step (3)kThe specific steps that online user in social networks competes subtask by mode of bid include：
(201) the initialization current discrete time is t=1, and density thresholding ρ=ε, wherein ε are a smaller constant, are enabled
Victor gathersOffline person's setEach social activity user UiRemuneration pi=0；
(202) as t≤max { e1,e2,...,em, repeat step 203-214；
(203) arbitrarily A is acted on behalf of in agent list AkAfter reaching the standard grade, AkA message MSG=(Γ can be sentk,ak,dk) arrive society
Hand over network, wherein ΓkTo act on behalf of AkThe unfinished subtask set of fashion of reaching the standard grade, akTo act on behalf of AkOn-line time, dkFor agency
AkDowntime, order act on behalf of AkOnline person set
(204) S is enabledkTo act on behalf of AkThe victor of selection gathers, initialization
(205) A is each acted on behalf ofkSocial networks in social user i reach the standard grade after, by acting on behalf of AkSubmit a bidding documents θi
=(ai,di,Γi,bi,Ak), wherein aiFor the on-line time of social user i, diFor the downtime of social user i, ΓiFor society
Hand over the interested subtask set of user i, biSubtask set Γ is completed for social user iiThe minimum remuneration gone for, Ak
For the agency belonging to social user i；Enable Ok=Ok∪ { i }, O'k←Ok\Sk；
(206) step 207-208 is repeated, until
(207) in AkSocial networks in all social user's set O' to reach the standard gradekSelect marginal value Vi(Sk)=V
(Sk∪{i})-V(Sk) maximum social user i, wherein V (Sk∪ { i }) it is social user's set Sk∪ { i } is to crowdsourcing platform
Value, V (Sk) it is victor's set SkTo the value of crowdsourcing platform；
(208) if the marginal value V that social the done tasks of user i are brought to platformi(Sk) and current density thresholding ρ ratio
Value meetsWhereinTo act on behalf of AkRemaining budget, then by society
User i is handed over to be added to victor's set SjIn set S, the remuneration of social user i is
(209) O' is enabledk←O'k\{i}；
(210) if social user i is offline, social user i is added in offline person's set S'；
(211) if acting on behalf of A there are arbitrarykIt is offline, then utilize its budget BkDensity thresholding ρ, weight are updated with offline person's set S'
Multiple step 212-213, until acting on behalf of AkSocial networks in all social users to reach the standard grade in moment t be all calculated；Otherwise,
Go to step 214；
(212) in arbitrary AkSocial networks in all online social user's set O'kMiddle selection marginal value Vi
(Sk)=V (Sk∪{i})-V(Sk) maximum social user i；
(213) according to the new remuneration of the current online victor of updated density thresholding ρ ' calculating, if meetingAndThen the remuneration of social user i is updated to
(214) t=t+1 is enabled.
The renewal process of density thresholding ρ is as follows in step (211)：
(301) social user's set is initialized
(302) from set S' in ψ selection there is maximumThe social user i, wherein V of valuej(ψ) is social user j
The marginal value brought to platform at set ψ, bjInterested subtask set Γ is completed for social user jjIt goes for
Minimum remuneration, j ∈ S', S' gather for offline person；
(303) if meeting conditionThen social user i is added in set ψ, i.e. ψ=ψ ∪ { i },
And go to step 302；Otherwise updated density thresholding ρ '=V (ψ)/B is enabledk。
Advantageous effect：Mobile crowdsourcing online motivational techniques disclosed by the invention based on social networks, by being chosen at society
It hands over the registration user that influence power is larger in network to go diffusion crowdsourcing task, allows enough social activities to use using effective incentive mechanism
Family is willing to participate in crowdsourcing task.Compared with prior art, it has the following advantages：1, for the first time using social networks come flooding duties with
Attract more social activity users to participate in the completion of task, is found more by diffusion only a small amount of registration user
Social user, improve the completion rate of task；2, a kind of method of assessment registration user force is provided, and selects to influence
The high registration user of power becomes agency, and task is diffused into its social networks to attract more social users by agency；3, it counts
Evaluation time complexity is low, and the time complexity of the reverse auction in the motivational techniques is O (max { maxj∈J|SNj|nm2,n2), it is
One complete multinomial time method has and calculates validity；4, personal financing is met to the motivational techniques of social user,
I.e. platform pays the true cost that the remuneration number of each victor is centainly more than or equal to consuming needed for social activity user, therefore
For attracting the social user in a large amount of social networks to have positive effect；5, the motivational techniques are anti-fraud, when other social activities
When user submits the true quotation of itself, even if some social user takes certain strategy false quotation, it will not make
The effectiveness of social activity user is got higher, therefore social user tends to submit the true quotation of itself.Anti-fraud is for preventing
Corner on the market or gang up plays an important roll.
Description of the drawings
Fig. 1 is the execution flow of the crowdsourcing process of double-layer structure in the present invention；
Fig. 2 is crowdsourcing platform selection agency from registration user in the present invention, and is the execution of each agent allocation budget
Flow；
Fig. 3 is the execution flow of online victor selection and remuneration computational algorithm in the present invention；
Fig. 4 is the execution flow of crowdsourcing platform update density thresholding ρ in the present invention.
Specific implementation mode
With reference to the accompanying drawings and detailed description, the present invention is furture elucidated.
Noun explanation：
Crowdsourcing platform：It is a kind of to issue task on the internet, and select social user to complete task from internet
System.Crowdsourcing platform is under the jurisdiction of some social networking website in the present invention, and the registration user of crowdsourcing platform is social networks registration
The subset of user.Crowdsourcing platform can obtain certain social network information, such as the topological structure of social networks.
Agency：User is registered by the high-impact that the present invention chooses, is the diffusion person of crowdsourcing task.
Social user：The user of crowdsourcing task is willing to participate in the social networks of agency；
The effectiveness of user：The difference of remuneration and the cost paid that user obtains.In the motivational techniques of anti-fraud, user
Cost be equal to user quotation.
Consider that a crowdsourcing platform, the crowdsourcing platform are that a social networking website possesses, and possesses a bulk registration user.
The extensive crowdsourcing task of crowdsourcing platform publication a batch, and register user itself and be not enough to complete this batch of task.Crowdsourcing at this time is flat
Platform will select a collection of user to become agency from registration user, and crowdsourcing task is diffused into its social networks using these agencies
Task is completed to attract more social users.
A kind of online motivational techniques of mobile crowdsourcing based on social networks disclosed by the invention, crowdsourcing platform with agency and
The crowdsourcing process of a double-layer structure is presented as between social user in agency and social networks, flow is as shown in Figure 1, step
It is as follows：
Step 1 is the task T of B to master budget, and crowdsourcing platform γ user of selection from registration user's set J constitutes generation
Set A is managed, wherein positive integer γ is constant；And it calculates and each acts on behalf of AkPay the budget B of user in its social networksk；
Crowdsourcing platform determines that the specific steps of agent list A include：
(101) initialization agent list A is empty set, s=γ, traversal registration user set J, according to going through for registration user j
History line duration section HjTimes of each registration user j to online task time section is calculated with online task time section H
Covering, wherein HjIt can be obtained according to the history online record of registration user, H=[0, max { e1,e2,...,em], registration user j
H is covered as to the time in online task time sectionjWith the time of coincidence section of H；j∈J；
(102) the selection arg max from registration user's set Jh∈J\A(Hh∩ H) it is user j, wherein operation I expressions ask two
The time of coincidence section of a time interval；
(103) pass through influence power calculation formulaInfluence powers of the user j in its social networks is calculated,
Middle lnfi jThe possibility of task is participated in for social user i in the social networks of user j, calculation formula isJac(Γj, i) and it is that user j reaches the standard grade the unfinished subtask set Γ of fashionk
Outstanding person between social activity user i set of tasks interested in its social networks blocks German number, and calculation formula isWherein IiIt, can be from social network user for the interested subtask set of social user i
It is extracted in Profile, ImaxFor the biggest impact factor, it is greater than 1 constant；SNjFor social user in user's j social networks
Set, | | indicate the operator of element number in set of computations；
(104) if Hj∩H>0 and ln fj/|Hj|>δ, wherein δ are constant, then by user j addition agent list A, and from
Remove in J in user j, H and removes Hj, the value of s subtracts 1；
(105) whenS ≠ 0, andWhen, step 101-104 is repeated, until constituting comprising γ agency
Agent list A.
(106) A is acted on behalf of to each of agent list Ak, calculate it and pay the budget B of user in its social networksk, Bk's
Calculation formula is Bk=(lnfk/∑q∈Alnfq)B。
Task T is decomposed into multiple subtasks by step 2, constitutes set of tasks Γ={ τ1,τ2,...,τm}；Appoint per height
Be engaged in τpIt is 0 at the beginning of ∈ Γ, end time ep, epFor the integral multiple of the unit time 1 of discretization, p=1..m；Crowdsourcing
Set of tasks Γ is distributed to each agency in agent list A by platform；
After arbitrary agency in step 3, agent list A reaches the standard grade, A is each acted on behalf ofkThe son that current time can be not yet completed
Set of tasks ΓkIt is sent to AkSocial networks, AkOnline user in social networks competes subtask by mode of bid；And
Remuneration is calculated for bid victor；K=1.. γ；
AkThe specific steps that online user in social networks competes subtask by mode of bid include：
(201) the initialization current discrete time is t=1, and density thresholding ρ=ε, wherein ε are a smaller constant, are enabled
Victor gathersOffline person's setEach social activity user UiRemuneration pi=0；
(202) as t≤max { e1,e2,...,em, repeat step 203-214；
(203) arbitrarily A is acted on behalf of in agent list AkAfter reaching the standard grade, AkA message MSG=(Γ can be sentk,ak,dk) arrive society
Hand over network, wherein ΓkTo act on behalf of AkThe unfinished subtask set of fashion of reaching the standard grade, akTo act on behalf of AkOn-line time, dkFor agency
AkDowntime, order act on behalf of AkOnline person set
(204) S is enabledkTo act on behalf of AkThe victor of selection gathers, initialization
(205) A is each acted on behalf ofkSocial networks in social user i reach the standard grade after, by acting on behalf of AkSubmit a bidding documents θi
=(ai,di,Γi,bi,Ak), wherein aiFor the on-line time of social user i, diFor the downtime of social user i, Γ i are society
It is that social user i completes the minimum remuneration that subtask set Γ i are gone for, A to hand over the interested subtask set of user i, bik
For the agency belonging to social user i；Enable Ok=Ok∪ { i }, O'k←Ok\Sk；
(206) step 207-208 is repeated, until
(207) in AkSocial networks in all social user's set O' to reach the standard gradekSelect marginal value Vi(Sk)=V
(Sk∪{i})-V(Sk) maximum social user i, wherein V (Sk∪ { i }) it is social user's set Sk∪ { i } is to crowdsourcing platform
Value, V (Sk) it is victor's set SkTo the value of crowdsourcing platform；
(208) if the marginal value V that social the done tasks of user i are brought to platformi(Sk) and current density thresholding ρ ratio
Value meetsWhereinTo act on behalf of AkRemaining budget, then by society
User i is handed over to be added to victor's set SjIn set S, the remuneration of social user i is
(209) O' is enabledk←O'k\{i}；
(210) if social user i is offline, social user i is added in offline person's set S'；
(211) if acting on behalf of A there are arbitrarykIt is offline, then utilize its budget BkDensity thresholding ρ, weight are updated with offline person's set S'
Multiple step 212-213, until acting on behalf of AkSocial networks in all social users to reach the standard grade in moment t be all calculated；Otherwise,
Go to step 214；
(212) in arbitrary AkSocial networks in all online social user's set O'kMiddle selection marginal value Vi
(Sk)=V (Sk∪{i})-V(Sk) maximum social user i；
(213) according to the new remuneration of the current online victor of updated density thresholding ρ ' calculating, if meetingAndThen the remuneration of social user i is updated to
(214) t=t+1 is enabled.
The renewal process of density thresholding ρ is as follows in step (211)：
(301) social user's set is initialized
(302) from set S' in ψ selection there is maximumThe social user i, wherein V of valuej(ψ) is that social activity user j exists
The marginal value brought to platform under set ψ, bjInterested subtask set Γ is completed for social user jjIt goes for most
Understatement is fulfilled, and j ∈ S', S' gather for offline person；
(303) if meeting conditionThen social user i is added in set ψ, i.e. ψ=ψ ∪ { i },
And go to step 302；Otherwise updated density thresholding ρ '=V (ψ)/B is enabledk。
Target subtask during step 4, victor execute, is sent to crowdsourcing platform by implementing result and obtains remuneration.
Claims (4)
1. a kind of online motivational techniques of mobile crowdsourcing based on social networks, which is characterized in that include the following steps：
(1) to the task T that master budget is B, crowdsourcing platform γ user of selection from registration user's set J constitutes agent list A,
Wherein positive integer γ is constant；And it calculates and each acts on behalf of AkPay the budget B of user in its social networksk；
(2) task T is decomposed into multiple subtasks, constitutes set of tasks Γ={ τ1,τ2,...,τm}；Each subtask τp∈Γ
At the beginning of be 0, end time ep, epFor the integral multiple of the unit time 1 of discretization, p=1..m；Crowdsourcing platform will appoint
Business set Γ is distributed to each agency in agent list A；
(3) after the arbitrary agency in agent list A reaches the standard grade, A is each acted on behalf ofkThe subtask that current time not yet completes can be gathered
ΓkIt is sent to AkSocial networks, AkOnline user in social networks competes subtask by mode of bid；And it is obtained to submit a tender
Victor calculates remuneration；K=1.. γ；
(4) target subtask during victor executes, is sent to crowdsourcing platform by implementing result and obtains remuneration.
2. a kind of online motivational techniques of mobile crowdsourcing based on social networks according to claim 1, which is characterized in that many
Packet platform determines that the specific steps of agent list A include：
(101) initialization agent list A is empty set, and s=γ, traversal registration user set J exist according to the history of registration user j
Line time interval HjEach registration user j is calculated with online task time section H to cover the time in online task time section,
Wherein HjIt can be obtained according to the history online record of registration user, H=[0, max { e1,e2,...,em], registration user j to
The time in line task time section is covered as HjWith the time of coincidence section of H；j∈J；
(102) the selection arg max from registration user's set Jh∈J\A(Hh∩ H) it is user j, when wherein operation I expressions ask two
Between section time of coincidence section；
(103) pass through influence power calculation formulaInfluence powers of the user j in its social networks is calculated, wherein
lnfi jThe possibility of task is participated in for social user i in the social networks of user j, calculation formula isJac(Γj, i) and it is that user j reaches the standard grade the unfinished subtask set Γ of fashionk
Outstanding person between social activity user i set of tasks interested in its social networks blocks German number, and calculation formula isWherein IiIt, can be from social network user for the interested subtask set of social user i
It is extracted in Profile, ImaxFor the biggest impact factor, it is greater than 1 constant；SNjFor social user in user's j social networks
Set；
(104) if Hj∩H>0 and ln fj/|Hj|>δ, wherein δ are constant, then agent list A are added in user j, and from J
Remove user j, removes H in Hj, the value of s subtracts 1；
(105) whenS ≠ 0, andWhen, step 101-104 is repeated, until constituting the generation for including γ agency
Manage set A.
(106) A is acted on behalf of to each of agent list Ak, calculate it and pay the budget B of user in its social networksk, BkCalculating
Formula is Bk=(lnfk/∑q∈Alnfq)B。
3. a kind of online motivational techniques of mobile crowdsourcing based on social networks according to claim 1, which is characterized in that step
Suddenly A in (3)kThe specific steps that online user in social networks competes subtask by mode of bid include：
(201) the initialization current discrete time is t=1, and density thresholding ρ=ε, wherein ε are a smaller constant, enable and winning
Person gathersOffline person's set, each social activity user UiRemuneration pi=0；
(202) as t≤max { e1,e2,...,em, repeat step 203-214；
(203) arbitrarily A is acted on behalf of in agent list AkAfter reaching the standard grade, AkA message MSG=(Γ can be sentk,ak,dk) arrive social network
Network, wherein ΓkTo act on behalf of AkThe unfinished subtask set of fashion of reaching the standard grade, akTo act on behalf of AkOn-line time, dkTo act on behalf of Ak's
A is acted on behalf of in downtime, orderkOnline person set
(204) S is enabledkTo act on behalf of AkThe victor of selection gathers, initialization
(205) A is each acted on behalf ofkSocial networks in social user i reach the standard grade after, by acting on behalf of AkSubmit a bidding documents θi=(ai,
di,Γi,bi,Ak), wherein aiFor the on-line time of social user i, diFor the downtime of social user i, ΓiFor social user
The interested subtask set of i, biSubtask set Γ is completed for social user iiThe minimum remuneration gone for, AkFor social activity
Agency belonging to user i；Enable Ok=Ok∪ { i }, O'k←Ok\Sk；
(206) step 207-208 is repeated, until
(207) in AkSocial networks in all social user's set O' to reach the standard gradekSelect marginal value Vi(Sk)=V (Sk∪
{i})-V(Sk) maximum social user i, wherein V (Sk∪ { i }) it is social user's set Sk∪ { i } to the value of crowdsourcing platform,
V(Sk) it is victor's set SkTo the value of crowdsourcing platform；
(208) if the marginal value V that social the done tasks of user i are brought to platformi(Sk) full with the ratio of current density thresholding ρ
FootWhereinTo act on behalf of AkRemaining budget then uses social activity
Family i is added to victor's set SjIn set S, the remuneration of social user i is
(209) O'k ← O' is enabledk\{i}；
(210) if social user i is offline, social user i is added in offline person's set S'；
(211) if acting on behalf of A there are arbitrarykIt is offline, then utilize its budget BkDensity thresholding ρ is updated with offline person's set S', repeats to walk
Rapid 212-213, until acting on behalf of AkSocial networks in all social users to reach the standard grade in moment t be all calculated；Otherwise, it redirects
To step 214；
(212) in arbitrary AkSocial networks in all online social user's set O'kMiddle selection marginal value Vi(Sk)=V
(Sk∪{i})-V(Sk) maximum social user i；
(213) according to the new remuneration of the current online victor of updated density thresholding ρ ' calculating, if meetingAndThen the remuneration of social user i is updated to
(214) t=t+1 is enabled.
4. a kind of online motivational techniques of mobile crowdsourcing based on social networks according to claim 3, which is characterized in that
The renewal process of density thresholding ρ is as follows in step (211)：
(301) social user's set is initialized
(302) from set S' in ψ selection there is maximumThe social user i, wherein V of valuej(ψ) is that social activity user j is gathering
The marginal value brought to platform under ψ, bjInterested subtask set Γ is completed for social user jjThe most understatement gone for
Reward, j ∈ S', S' gather for offline person；
(303) if meeting conditionThen social user i is added in set ψ, i.e. ψ=ψ ∪ { i }, and jumped
Go to step 302；Otherwise updated density thresholding ρ '=V (ψ)/B is enabledk。
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| CN109767060A (en) * | 2018-12-04 | 2019-05-17 | 嘉兴国电通新能源科技有限公司 | A kind of crowdsourcing motivational techniques based on End-user relevance and accuracy |
| CN110275762A (en) * | 2019-04-30 | 2019-09-24 | 北京媒球信息科技有限公司 | Calculation method, server, mobile terminal and device |
| CN111083675A (en) * | 2019-12-03 | 2020-04-28 | 西北工业大学 | Resource allocation method based on social perception in industrial Internet of things |
| CN112418803A (en) * | 2020-11-25 | 2021-02-26 | 南京大学 | Crowd-sourced tester recruitment method based on social network |
| CN113222391A (en) * | 2021-05-06 | 2021-08-06 | 南京财经大学 | Global scoring method for registrants of crowdsourcing business platform |
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2018
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| CN109767060A (en) * | 2018-12-04 | 2019-05-17 | 嘉兴国电通新能源科技有限公司 | A kind of crowdsourcing motivational techniques based on End-user relevance and accuracy |
| CN109767060B (en) * | 2018-12-04 | 2022-12-09 | 嘉兴国电通新能源科技有限公司 | Crowdsourcing incentive method based on user relevance and accuracy |
| CN110275762A (en) * | 2019-04-30 | 2019-09-24 | 北京媒球信息科技有限公司 | Calculation method, server, mobile terminal and device |
| CN111083675A (en) * | 2019-12-03 | 2020-04-28 | 西北工业大学 | Resource allocation method based on social perception in industrial Internet of things |
| CN112418803A (en) * | 2020-11-25 | 2021-02-26 | 南京大学 | Crowd-sourced tester recruitment method based on social network |
| CN112418803B (en) * | 2020-11-25 | 2024-04-12 | 南京大学 | Crowd-sourced tester recruitment method based on social network |
| CN113222391A (en) * | 2021-05-06 | 2021-08-06 | 南京财经大学 | Global scoring method for registrants of crowdsourcing business platform |
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