CN113222391A - Global scoring method for registrants of crowdsourcing business platform - Google Patents
Global scoring method for registrants of crowdsourcing business platform Download PDFInfo
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- CN113222391A CN113222391A CN202110492153.2A CN202110492153A CN113222391A CN 113222391 A CN113222391 A CN 113222391A CN 202110492153 A CN202110492153 A CN 202110492153A CN 113222391 A CN113222391 A CN 113222391A
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- 238000013077 scoring method Methods 0.000 title claims abstract description 12
- 230000009193 crawling Effects 0.000 claims abstract description 7
- 230000010354 integration Effects 0.000 claims abstract description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000006855 networking Effects 0.000 claims 3
- 238000000034 method Methods 0.000 description 7
- 238000000605 extraction Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012358 sourcing Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention discloses a global scoring method for registered practitioners on a crowdsourcing business platform, in particular to a global scoring method for registered practitioners on the crowdsourcing business platform based on multiple social networks, which comprises the steps of extracting crowdsourcing practitioner information, constructing the multiple social networks of the crowdsourcing practitioners and constructing the global scoring based on the multiple social networks of the crowdsourcing practitioners, wherein each step independently realizes the self function, the operation result of each step provides input for the subsequent steps, the information of the practitioners registered on the crowdsourcing business platform, including identities and original grading information on the crowdsourcing platform, is extracted from the crowdsourcing business platform through a crawling program, so that the sharing and integration of the grading values of the practitioners among the crowdsourcing business platform are realized, the condition that the newly registered practitioners have no grading can be avoided, the reliability of the score value can be improved, and the practitioners are promoted to cooperate with each other to complete complex tasks.
Description
Technical Field
The invention relates to a global scoring method for a crowdsourcing business platform registrant, and belongs to the technical field of internet crowdsourcing.
Background
Crowdsourcing is a rapidly developing new computing concept and business model that can collaborate to accomplish work tasks by aggregating the intelligence and capabilities of the masses on the internet. Currently, there are many specialized crowdsourcing business platforms that offer crowdsourcing services, such as Upwork, freelance, Crowdworks, GitHub, pig barring, etc. Users publish task information (including task content, skill requirements, deadline, budget amount and the like) on the crowdsourcing business platform, and practitioners can register personal information and then find tasks on the crowdsourcing business platform. And then the task is distributed to a registered practitioner with certain skills, the practitioner returns the result to the task publisher after completing the task, and the task publisher pays a reward according to the quality of the registered completed task. The registered practitioner can obtain a certain score in addition to the reward. This score may affect the likelihood of a practitioner obtaining a new crowd-sourced task in the future, with a practitioner having a high score being more likely to obtain a sexual task.
The existing crowdsourcing business platform independently manages the information of registered practitioners, and particularly, the scoring information of the practitioners is only displayed on the registered websites. The score for each practitioner is determined by the number and quality of their past completed tasks on the registered website. Crowdsourced business platforms do not integrate and share the scoring information of practitioners efficiently with each other. Such a case where the practitioner score information cannot be shared by independent management leads to the following problems:
(1) the score value of a practitioner is only determined by the historical situation of the completed tasks of the practitioner on one platform, so the score of the newly registered practitioner cannot be obtained, and the newly registered practitioner cannot obtain the opportunity of task distribution all the time, and thus the situation that the tasks are monopolized by some old practitioners all the time occurs.
(2) Some practitioners can obtain the task distribution quantity in a short period of time in a manual mode, so that the score value of the practitioners can be improved.
(3) Since the scoring information of the practitioners of each crowdsourcing business platform is isolated and shielded from each other, the practitioners registered on different platforms cannot cooperate effectively, so that complex tasks which can be executed only by cooperation of a plurality of practitioners cannot be finished smoothly.
Recent research results show that many practitioners in a crowdsourcing platform can often communicate through social networks, and that many practitioners have also joined various social network groups. Therefore, crowdsourcing in social networks is a problem of intense interest and research in the industry. In particular, in recent years, as the variety of social networks and the diversity of user groups increase, the interactive relationship between practitioners exhibits characteristics of multiple social networks. The population of practitioners in crowdsourcing systems often have multiple social connections through various social media (e.g., Facebook, Twitter, WeChat, microblog, forum, etc.) at the same time, each in the context of co-workers, friends, family, and the multiple social networks formed by daily work and life. For example, in the open source community crowdsourcing platform GitHub, a crowd sourcing practitioner population for a certain software project often has not only a software development cooperative relationship but also other classmatic or co-workers relationships.
Disclosure of Invention
The invention provides a global scoring method for a crowdsourcing business platform registrant, which is used for overcoming the defects that in the prior art, effective practitioner scoring information sharing cannot be realized between traditional crowdsourcing business platforms, so that scoring is localized and unreliable.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention discloses a global scoring method for crowdsourcing business platform registrants based on multiple social networks, which comprises the following steps: the method comprises the steps of extracting crowdsourcing practitioner information, constructing a multiple social network of crowdsourcing practitioners, and constructing global scoring based on the multiple social network of crowdsourcing practitioners, wherein each step independently realizes the function of the step, and the operation result of each step provides input for the subsequent step.
Further, the method comprises the following steps: extracting crowdsourcing practitioner information, wherein the extraction of the practitioner information (including identity information and original grading information on a crowdsourcing platform) is crawled from crowdsourcing websites through a crawling program; in addition, other various social network information (such as Facebook, LinkedIn, DBLP and the like) of the practitioner is crawled, and the social network information comprises social information of friends, collaborators, colleagues and the like.
Further, the second step of the method is: and constructing multiple social networks of crowdsourcing practitioners, wherein the multiple social networks of the practitioners are constructed according to the information of the crowdsourcing website practitioners and the information of the social networks of the practitioners, which are obtained in the previous step. Nodes in the multiple social networks represent practitioner information (including personal information such as identity and local score of the practitioner), and edges in the networks represent multiple social network relationships of the practitioner (including social relationship type and weight of the social relationship).
Further, the third step of the method is: based on the global scores of the multiple social networks of the crowdsourcing practitioners, the global score of a practitioner is comprehensively determined by the original score of the practitioner on the registered crowdsourcing business platform, the original scores of other practitioners having multiple social network relationships with the practitioner, and the weight of the social network relationships.
The invention has the following beneficial effects: the method comprises the steps of sharing and fusing practitioner grading information among crowdsourcing business platforms by utilizing multiple social network relations among practitioners, building multiple social networks of the practitioners by crawling information of the practitioners in different crowdsourcing business platforms and social network information of different types of the practitioners, integrating and fusing grading information of the practitioners on the registered crowdsourcing business platforms, and finally obtaining overall scores of the practitioners among the crowdsourcing business platforms.
The method and the system realize the sharing and integration of the score values of the practitioners among the crowdsourcing business platforms, not only can avoid the condition that the newly registered practitioners have no score, but also can improve the reliability of the score values and promote the practitioners to mutually cooperate to complete complex tasks.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a system flow diagram of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, a global scoring method for a crowdsourced commerce platform registrant based on multiple social networks includes: the method comprises the steps of extracting crowdsourcing practitioner information, constructing a multiple social network of crowdsourcing practitioners, and constructing global scoring based on the multiple social network of crowdsourcing practitioners, wherein each step independently realizes the function of the step, and the operation result of each step provides input for the subsequent step.
1. Extraction of crowdsourcing practitioner information, firstly, extracting practitioner information (including identity and original grading information on the crowdsourcing platform) registered on the crowdsourcing business platform from the crowdsourcing business platform through a crawling program, and assuming that a registered practitioner j on the platform i can use ID to identify the registered practitioner jijShowing that the original scoring information is Sij(namely, registering local scoring information of the practitioner j on the crowdsourcing platform i, wherein the scoring is carried out by a task publisher according to the condition that the practitioner performs tasks in the past, so that the scoring value is higher as the quantity and quality of the tasks completed by the practitioner in the past are higher), and then crawling other various social network information (such as Facebook, LinkedIn, DBLP and the like) of the practitioner, including social information such as friends, collaborators, colleagues, academic relations and the like; each piece of information of the social network of the practitioner may be composed of a quadruple<IDij,IDxy,Tij,xy,Wij,xy>Wherein IDijIndicating registered practitioners j, ID on the crowdsourced commerce platform ixyRepresenting registered practitioners y, T on a crowdsourced commerce platform xij,xyIndicating IDijAnd IDxyKind of social relationship between, Wij,xyThe weight (by ID) representing the social relationshipijAnd IDxyThe frequency of interactions along the social relationship, etc.).
2. Constructing a multiple social network of crowdsourcing practitioners, namely constructing the multiple social network N of the practitioners according to the crowd-sourcing commodity platform practitioner information and the practitioner social network information obtained in the previous step, wherein nodes in the multiple social network N represent practitioner information, and edges in the network represent social network relationships (including social relationship types and social relationship weights) of the practitioners; if two practitioners have various social network relationships, multiple edges can be formed between corresponding nodes of the two practitioners; assuming that the total number of categories of social relationships among all registered practitioners of all the crawled crowdsourced commerce platforms is m, the constructed multi-network comprises m sub-networks, and according to the m sub-networksSub-expression is N ═ N1,N2,…,Nm}；Nk(1≤k≤m)＝<Ak,Ek>Represents the kth sub-network, i.e. the social network of the practitioner constituted by the kth social relationship, where AkSet of IDs representing registered practitioners { IDij}，EkIs a collection of edges in the subnetwork, each edge can be represented as a quadruple as described in the previous step<IDij,IDxy,Tij,xy,Wij,xy>。
3. Based on the global scoring of the multiple social networks of the crowdsourcing practitioners, since the practitioners cooperate with each other through social network relationships, the global scoring value of a practitioner is comprehensively determined by the original scoring value of the practitioner on the registered crowdsourcing business platform, the original scoring values of other practitioners with multiple social network relationships with the practitioner, and the weight of the social network relationships. Assume practitioner IDijThe original score value on the crowdsourced commerce platform i is SijPractitioner IDxyThe original score value on the crowdsourced commerce platform x is Sxy(ii) a Assume IDijAnd IDxyHave a social relationship of class k between them, then δij,xy k1, otherwise δij,xy k0. Then the practitioner IDijGlobal value of (by G)jExpression) the calculation method is as follows:
wherein beta is1And beta2Is two parameters, the value range is more than 0, the relative importance of the original score value of the practitioner in the scoring system and the original score values of other practitioners in the multiple social network relationship is represented, beta1And beta2Can be set according to specific environmental requirements.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A global scoring method for registrants of a crowdsourcing business platform is characterized by comprising the following steps: s1: extracting information of registered practitioners of the crowdsourcing business platform;
s2: constructing multiple social networks of crowdsourcing practitioners;
s3: score integration and global scoring based on multiple social networks of crowdsourcing practitioners.
2. The global scoring method for registrants of multi-social networking crowdsourcing business platform of claim 1, wherein the step S1 is performed as follows:
s11, extracting information of the practitioner registered on the crowdsourcing business platform from the crowdsourcing business platform through a crawling program, wherein the information comprises identity and original scoring information on the crowdsourcing platform,
a registered practitioner j on the crowdsourced commerce platform i,
ID for its identityijIt is shown that,
the original scoring information is Sij，
Namely, local scoring information of the registered practitioner j on the crowdsourcing business platform i;
s12, crawling other social network information of the practitioner, wherein each piece of information of the social network of the practitioner is composed of a quadruple<IDij,IDxy,Tij,xy,Wij,xy>，
Wherein the IDijRepresenting registered practitioners j on the crowdsourced commerce platform i,
IDxyrepresenting registered practitioners y on the crowdsourced commerce platform x,
Tij,xyindicating IDijAnd IDxyThe kind of social relationship between them,
Wij,xya weight value representing the social relationship is determined,
by IDijAnd IDxyAlong the social relationship.
3. The global scoring method for registrants of multi-social networking crowdsourcing business platform of claim 2, wherein the step S2 is performed as follows:
the total number of the social relationship types among all the registered practitioners of all the crawled crowdsourced commerce platforms is m, the constructed practitioner multi-social network comprises m sub-networks,
sequentially expressed as N ═ N1,N2,…,Nm}；Nk(1≤k≤m)＝<Ak,Ek>Represents the kth sub-network, i.e. the social network of the practitioner constituted by the kth social relationship,
the node in N represents practitioner information, the edge in the network represents the social network relationship of the practitioner, and the social network relationship type and the weight of the social relationship are contained;
wherein A iskSet of IDs representing registered practitioners { IDij}，EkIs a collection of edges in the subnetwork, each edge represented as a quadruple<IDij,IDxy,Tij,xy,Wij,xy>。
4. The global scoring method for registrants of multi-social networking crowdsourcing business platform of claim 1, wherein the step S3 is performed as follows:
practitioner IDijThe original score value on the crowdsourced commerce platform i is Sij，
Practitioner IDxyThe original score value on the crowdsourced commerce platform x is Sxy；
IDijAnd IDxyHave a social relationship of class k between them, then δij,xy k1, otherwise δij,xy k＝0，
Then the practitioner IDijGlobal value of (by G)jExpression) the calculation method is as follows:
wherein beta is1And beta2Is two parameters, and the value range is more than 0.
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