CN107391585A - Method is recommended in a kind of location-based micro- mutual assistance - Google Patents
Method is recommended in a kind of location-based micro- mutual assistance Download PDFInfo
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Abstract
The invention belongs to task recommendation technical field, is specially that method is recommended in a kind of location-based micro- mutual assistance.The characteristics of present invention is first between complex disablement people and volunteer's mutual assistance and demand relation, the data of structuring are established to represent various models；Then analyzing influence user receives the factor of aspiration task and by its quantization means on the basis of model, finally establishes the Generalization bounds towards disabled person and volunteer, and corresponding aspiration task recommendation is carried out for different users.One aspect of the present invention can solve the problem that the number difference between disabled person and volunteer, on the other hand also can quickly help disabled person to solve the trifling affairs in life, accomplish " whole people's aspiration " and " public good nearby ".It is that the present position of the users such as disabled person, volunteer, interest characteristics, historical record and the suggested design proposed have been merged by extending existing recommended technology.
Description
Technical field
The invention belongs to task recommendation technical field, and in particular to a kind of recommendation method suitable for volunteer's task.
Background technology
With the continuous improvement of people's living standards, this special disadvantaged group of disabled person are increasingly by society
Concern.The help whether disabled person can obtain volunteer in time has become a mark for weighing Volunteer service quality
It is accurate.Voluntary service has also developed into low developed area by original developed country gradually, the 3rd using voluntary service as core
Door is also become in order to which various countries most pay attention to, with the fastest developing speed field.Shown according to newest data, China disabled person
Total number of persons is about 85,020,000 people.But according to incompletely statistics China registration
Volunteer's number only only has more than 5,500 ten thousand, although the annual voluntary service provided for society more than 300,000,000 hours, puts down
Arrive each disabled person and be but less than 4 hours with it.In addition, the mode of volunteers' activity is first by public good group
Hair-weaving plays an activity, and volunteers respond activity and called to help disabled person.And volunteer activities are all more or less with certain
Social and communality, this resulted in disabled person commonweal organizations activity time in can just obtain centralization help,
But want help but can not find suitable volunteer in daily life.Just because of the number between disabled person and volunteer
Difference and inconsiderate to the daily demand of disabled person, if being only that can not meet that disabled person is normal completely by these volunteers
Demand.
Traditional recommendation method includes content-based recommendation, the recommendation based on collaborative filtering and based on hybrid-type recommendation
Technology etc., it is widely used in miscellaneous system, and obtains good effect.But for volunteer and residual
There is problems for traditional recommendation method in terms of disease people.First, demand can not meet.The user of disabled person and volunteer are special
Different property and new customer problem, traditional recommended technology had not both fitted within the feature of disabled person and volunteer user, again can not and
When the new user of acquisition interest demand, it is difficult to recommend the content for really meeting disabled person's user interest demand.Secondly, can not
Meet that disabled person's is ageing.Traditional recommendation method, whether content-based recommendation, the recommendation based on collaborative filtering or
All it is a kind of recommendation based on information on line, these methods are to nets such as text, video, audios based on hybrid-type recommended technology
Well, but but it is impossible to meet its timeliness in face of volunteer activities between disabled person and volunteer for the recommendation efficiency of network resource
Property.Because the volunteer activities that disabled person initiates are based on the request under line mostly, and if according to traditional way of recommendation not
Have and consider this special contextual information of geographical position, lead to not meet that user's is ageing.Again, volunteer is caused
It is lost in.Due to the deficiency of recommended technology, volunteer's needs when receiving an assignment are caused to spend many extra times and essence
Power, allow them gradually to lose the enthusiasm to aspiration, long-acting voluntary service mechanism can not be set up.These reasons result in
Traditional recommended technology can do nothing to help the help that disabled person meets its daily life, also can do nothing to help volunteer and efficiently receives will
Hope task, cause negative social benefit.
The content of the invention
In order that the progress of the aspiration task more effective between disabled person and volunteer, the present invention is existing by extending
A kind of some recommended technologies, it is proposed that location-based micro- mutual assistance task recommendation method.This method can not only be directed to disabled person
With the essential characteristic of volunteer, meet disabled person's requirement ageing to task, reduce the volume that volunteer is paid volunteer activities
Outer energy.
Location-based micro- mutual assistance task recommendation method proposed by the present invention, between complex disablement people and volunteer's mutual assistance
Feature and demand relation, the data of structuring are established to represent various models；Analyzing influence user connects on the basis of model
By aspiration task factor and by its quantization means；The Generalization bounds towards disabled person and volunteer are completed, for different use
Family carries out corresponding aspiration task recommendation.Comprise the following steps that：
（One）Establish the data model of structuring
Before location-based micro- mutual assistance recommendation method is designed, first choice needs serious analysis targeted customer, includes the spy of user
Sign, required various information and its feature of the user when performing volunteer activities, established then according to these features corresponding
The data model of structuring, the integrality of only these basic data mechanisms are protected, can just be advantageous to recommendation method to
The correct extraction of family feature.It is on the contrary then recommendation method hydraulic performance decline can be caused.
The data model established for the scene in volunteer activities, the present invention, including user（UserModel）, be good at
（Personality）, group（Group）, task（Request）, recommend（Recommend）, notice（Notification）, angle
Color（Role）Deng.
User model（UserModel）, as the basis of platform, nature, efficient, orderly use are established in customer-centric
Family interface contributes to user to be played a key effect in follow-up related issue, receiving, feedback, recommendation mission bit stream, in this hair
It is bright it is middle user model is represented using five-tuple, it is specific as follows：
UserModel = <UserID, Personality, Role, Group, Request>
Wherein, what UserID was represented is the unique identifier of user in platform, generation in the follow-up relevant user data of model,
The increase used in when collecting, feed back, deletion, modification, lookup etc. be required for by the use of the unique identifier of this user as
The Correlation Criteria of database.
It is described to be good at model（Personality）, be an all essential informations relevant with user set, include use
The feature of two aspects in family：On the one hand it is objective attribute possessed by each platform user, is on the other hand to embody to use householder
See the preference of feature（preference）, it is specific as follows shown：
Personality=<UserName, Passwd, Age, Gender, IDNumber, Preference…>
Wherein, UserName refers to user name, and Passwd is the password that user is set, and Age is the age of user, Gender
It is the sex of user, IDNumber is the numbering of user.Preference refers to the content that user's subjectivity is good at, by user certainly
Row is set.
The actor model（Role）, for representing different roles（Role）The different rights possessed, content include
Character types（RoleType）And role name（RoleName）, it is specific as follows shown：
Role = <RoleType, RoleName>
The cohort model（Group）, including to the effect that group number（GroupID）, which user be present in group
（GroupUserRelation relation tables）, founder（Creator）, creation time（CreatTime）Etc. content, it and user
It is the relation of multi-to-multi, it is specific as follows shown：
Group=<GroupID, GroupName, Creator, CreatTime, GroupUserRelation>
The task model（Request）, the issuing and receiving for storing each user of the task, mainly including mission number
（RequestID）, task essential information（Character）, safety code（Code）, notice（Notification）, recommend
（Recommend）Similarly be the relation of multi-to-multi with user etc. content, it is specific as follows shown in：
Request = < RequestID, Character, Code, Notification, Recommend>
The notification model（Notification）, most important marketing methods are runed as mobile terminal application, to application message
Paid attention to by more and more Mobile solution developers, a task possesses multiple notices, and is then corresponding to each notifying
One task, it and task model are one-to-many relations, include notice numbering（NotificationID）, corresponding to notice
Mission number（RequestID）And content of announcement（Content）Deng specific as follows shown：
Notification = < NotificationID, RequestID, Content>
The recommended models（Recommend）, the recommendation information that is generated for storage system according to the demand of user mainly includes
Recommendation number（RecommendID）, it is recommended user（Rec_User）, recommend the time（Time）, recommended mission number
（RequestID）And recommend whether task is received（Is_Accepted）It is similar with notification model etc. information, it and task
Model is also one-to-many relation, specific as follows shown：
Recommend= < RecommendID, Rec_User, Time, RequestID, Is_Accepted>。
（Two）It is determined that influenceing the factor recommended, and quantified
When formulating task recommendation strategy, it is necessary first to which it is to influence the factor that user receives an assignment that what, which understands, and this will be helpful to push away
Recommend method maker be fully understood by user to volunteer task expection and these factors to the influence degree that receives an assignment.Cause
This, analyzes the factor that volunteer and disabled person receive an assignment, it is quantified as to specific user characteristics index, structure as far as possible comprehensively
Build up complete evaluation system.
When whether volunteers receive an assignment, their behavior mainly includes two aspects：It is in terms of supervisor and objective
In terms of sight.
Subjective aspect：Whether have the ability to complete, publisher and my relation, preference（Refer to and participate in what kind of activity）、
The group of addition.
Objective aspects：Task distance, the fraction put on someone's head.
Therefore, after being received an assignment for these influence users, a sequence, the difference from important to secondary have been carried out to it
It is：Task distance, whether have the ability to complete, publisher and my relation, preference, the group added, put integration on someone's head.In face of these
Factor, it is quantified as to objective fertilizer index respectively, it is specific as shown in table 1.
Table 1：Dimension is influenceed to change with objective indicator
The wherein particularly importantly position of user, by corresponding investigation, user thinks, it is them that task is volunteered in one kilometer
Acceptable distance, their dislike can't be caused, therefore, here, the distance of task will be defined within one kilometer.
（Three）Location-based task recommendation
After the objective indicator that each user's receiving is recommended is influenceed accordingly, corresponding task recommendation is exactly carried out.
The specific steps of recommendation：
（1）Polling user obtains customer position information.It can find out from above-mentioned index, the distance between user and task are will
Whether hope person receives an assignment the information of main consideration, therefore, by constantly obtaining the geographical location information of user, calculates number
According in storehouse whether there is one kilometer within other people initiate aspiration task, if there is then go in next step, otherwise without
Recommend；
（2）Database is inquired about, obtains once receiving for task of user, the group added and the hobby filled in registration
Content, these contents are possible to be not present for new user, but these have no effect on recommendation；
（3）Satisfactory all alternative tasks in traversal step 1, the fraction of each task is calculated using Scoring method；
Scoring method is a method based on weight, there is different computational methods for different tasks, is specifically：Calculate first
User and the distance in task place, the weighted value highest of distance, specially 1000-distance）/ 20.0, i.e., if task with
User distance is 700 meters, then this result is 15 points；Then if user adds the group that task is issued, then along with 6
Point；If this task is the hobby content that user fills in registration, along with 4 points；Exist if this task is user
The content received, then along with 2 points；Last then be that task puts integration on someone's head, weight is minimum, for score/2.5 points；
（4）The maximum task of above-mentioned Scoring method mid-score is selected, and recommends corresponding user.
Beneficial effects of the present invention：
1st, comprehensively analyze and quantified user, the feature and relation of aspiration task, the index constructed is more comprehensive；
The 2nd, a kind of higher location-based micro- mutual assistance of accuracy is provided recommend method；
3rd, volunteer is helped, to complete volunteer activities, to be advantageous to the foundation of permanent mechanism with minimum additionally paying.
Brief description of the drawings
Fig. 1 is the structural data framework of this recommendation method rope structure.
Fig. 2 is the integrated stand composition for the platform realized based on recommendation method.
Fig. 3 is the composition that recommendation factor is influenceed in this recommendation method.
Fig. 4 is the particular flow sheet of task recommendation method.
Embodiment
In order that technical problems, technical solutions and advantages to be solved are more clearly understood, tie below
Accompanying drawing 3 and embodiment are closed, the present invention will be described in detail.It should be noted that specific embodiment described herein
Only to explain the present invention, it is not intended to limit the present invention.
Embodiment：Using terminal device of the smart mobile phone as volunteer user, with the eclipse under windows platform
The exploitation of Android and iOS cell-phone customer terminals is developed respectively with the xcode under OS system X, from mysql as backstage
Database, using Java and Objective-C as programming language, location-based micro- mutual assistance platform has been researched and developed to realize
The tasks such as issue, reception, recommendation to volunteering task.
As shown in figure 1, the concrete scene to be received an assignment in face of volunteer, has initially set up the knot based on user, activity etc.
Structure data model, mainly includes several large-sized models such as user, group, task, recommendation, notice, and these models ensure jointly
The integrality of system-based data model.
Fig. 2 is the general frame figure of micro- mutual assistance platform, and micro- mutual assistance platform is broadly divided into four-layer structure, distinguished from top to bottom
It is boundary layer, middle core logic layer and the data Layer of model layer and bottom.Boundary layer is as the displaying with user mutual
The information at interface, mainly display platform.Logical layer and model layer are the intermediate layers of whole system, and the portion of the system core
Point：Model layer is that the above-mentioned model referred to establishes process.Core logic layer is mainly the security mechanism of secure system safety, swashed
The Generalization bounds of the incentive mechanism and core of encouraging user are formed.And database layer is then to carry to produce number in storage platform
According to effect, including related Basic Information Table such as task list, user's table and the relation table of correlation provide basic take for platform
Business.
Fig. 3 is to influence some factors for receiving an assignment of user, wherein the distance in contextual information be from system constantly to
User's poll simultaneously calculates what is obtained automatically, and preference is then that user fills in platform register account number, and fraction is then disabled human hair
It has been pre-filled with during cloth task, group is then that volunteer user adds during daily interaction.System is by extracting this
A little contextual informations, with reference to current task list and the historical data of targeted customer, generate corresponding recommendation task.
Fig. 4 is then the flow chart of specific task recommendation method, filters out existing satisfactory task nearby first,
If then carrying out showing that the best suiting user of the task is recommended using Scoring method.Simultaneously by corresponding recommendation results
It is stored in database, subsequently to be recommended.
Claims (2)
1. method is recommended in a kind of location-based micro- mutual assistance, it is characterised in that the spy between complex disablement people and volunteer's mutual assistance
Point and demand relation, the data of structuring are established to represent various models；Analyzing influence user receives on the basis of model
The factor of aspiration task and by its quantization means, completes the Generalization bounds towards disabled person and volunteer, for different users
Carry out corresponding aspiration task recommendation.
2. method is recommended in location-based micro- mutual assistance according to claim 1, it is characterised in that concrete operation step is such as
Under：
（One）Establish structural data model
First, analyze targeted customer, include the feature of user, required various information of the user when performing volunteer activities and
Its feature, corresponding structural model is established according to these features and information；Model includes user model UserModel, is good at
Model Personality, cohort model Group, task model Request, recommended models Recommend, notification model
Notification, actor model Role；Wherein：
The user model UserModel, as the basis of platform, nature, efficient, orderly user are established in customer-centric
Interface, so that user carries out follow-up related issue, receiving, feedback, recommendation mission bit stream, user's mould is represented using five-tuple
Type, it is specific as follows：
UserModel = <UserID, Personality, Role, Group, Request>
Wherein, what UserID was represented is the unique identifier of user in platform, generation, remittance in the follow-up relevant user data of model
Increase, deletion, modification, the lookup used in when collection, feedback, are required for being used as data by the use of the unique identifier of this user
The Correlation Criteria in storehouse；
It is described to be good at model Personality, it is the set of an all essential informations relevant with user, comprising user two
The feature of aspect：On the one hand it is objective attribute possessed by each platform user, is on the other hand to embody user's subjective characteristics
Preference, it is specific as follows shown in：
Personality=<UserName, Passwd, Age, Gender, IDNumber, Preference…>
Wherein, UserName refers to user name, and Passwd is the password that user is set, and Age is the age of user, and Gender is
The sex of user, IDNumber are the numberings of user；Preference is the content that user's subjectivity is good at, and is voluntarily set by user
Put；
The actor model Role, the different rights possessed for representing different roles are specific as follows shown：
Role = <RoleType, RoleName>
Wherein, RoleType is character types, and RoleName is role name；
The cohort model Group, it is the relation of multi-to-multi with user, it is specific as follows shown in：
Group=<GroupID, GroupName, Creator, CreatTime, GroupUserRelation>
Wherein, GroupID is group number, and it in group in the presence of which user is relation table that GroupUserRelation, which is,
Creator is founder, and CreatTime is creation time；
The task model Request, issuing and receiving for storing each user for task, with user and multi-to-multi
Relation, it is specific as follows shown：
Request = < RequestID, Character, Code, Notification, Recommend>
Wherein, RequestID mission numbers, Character are task essential information, and Code is safety code, Notification
For notice, Recommend is recommendation；
The notification model Notification, a task possess multiple notices, and corresponding to each notifying are then one and appoint
Business, it and task model are one-to-many relations, specific as follows shown：
Notification = < NotificationID, RequestID, Content>
Wherein, NotificationID numbers for notice, and RequestID is mission number corresponding to notice, and Content is notice
Content；
The recommended models Recommend, the recommendation information generated for storage system according to the demand of user, with notification model
Similar, it and task model are also one-to-many relation, specific as follows shown：
Recommend= < RecommendID, Rec_User, Time, RequestID, Is_Accepted>
Wherein, RecommendID is recommendation number, and Rec_User is recommended user, and Time is to recommend time, RequestID
To be recommended mission number, Is_Accepted is whether recommendation task is received；
（Two）It is determined that influenceing the factor recommended, and quantified
When whether volunteers receive an assignment, their behavior is that the factor for influenceing to recommend includes two aspects：Supervisor side
Face and objective aspects；
Subjective aspect：Whether have the ability completion, publisher and my relation, preference, the group added；
Objective aspects：Task distance, the fraction put on someone's head；
The factor recommended for these influences, after user receives an assignment, is ranked up to it, is respectively from important to secondary：Appoint
Business distance, whether have the ability to complete, publisher and my relation, preference, the group added, put integration on someone's head；In face of these factors,
It is quantified as to objective fertilizer index respectively；
（Three）Location-based recommendation
After the objective indicator that each user's receiving is recommended is influenceed accordingly, corresponding task recommendation is carried out；Recommend
Flow be：
（1）Polling user obtains customer position information, because the distance between user and task are whether volunteer receives an assignment
The information of main consideration, therefore, by constantly obtaining the geographical location information of user, calculate and whether there is one in database
The aspiration task that other people initiate within kilometer, if there is then going in next step, otherwise without recommending；
（2）Database is inquired about, obtains once receiving for task of user, the group added and the hobby filled in registration
Content；
（3）Traversal step（1）In satisfactory all alternative tasks, calculated using Scoring method each task point
Number；
（4）The maximum task of above-mentioned Scoring method mid-score is selected, recommends corresponding user.
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