Difference between revisions of "Winter Milestone 5 @niranga"

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(How is the system solving critical problems)
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== System ==
 
== System ==
  
'''Introducing the workers skill to the Boomerang Ranking'''
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'''Introducing the workers skills to the Boomerang Ranking'''
 
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=== Brief introduction of the system ===  
 
=== Brief introduction of the system ===  
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* However, among those workers, there can be workers who are not familiar with those tasks because those workers previously have worked with the requester on some other type of tasks( Surveys, Bookkeeping, etc.)
 
* However, among those workers, there can be workers who are not familiar with those tasks because those workers previously have worked with the requester on some other type of tasks( Surveys, Bookkeeping, etc.)
  
* So, if the requester selects one of the higher rating worker not familiar with the task, there can be a quality issue.
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* So, if the requester selects one of the higher rating worker not familiar with the task, there can be a quality issue. To mitigate the risk we proposed to introduce a task category filter option to Boomerang system.  
  
 
=== How is the system solving critical problems ===  
 
=== How is the system solving critical problems ===  
  
To mitigate the risk
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When a requester create a task he should choose a category relevant to that particular task. For example, if it's a image tagging task, task should go under 'Image tagging' category etc. So when the requester post this task to Crowd sourced market place, The system choose highly rated workers first and filter them according the category they have rated. Then Boomerang recommend the workers who has higher rating in that particular category. That way, the requester will get the most suitable higher rated workers first.
 
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=== Introducing modules of the system ===
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Below, we introduce the three main crowdsourcing
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applications that Twitch supports. The first, Census,
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attempts to capture local knowledge. The following two,
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Image Voting and Structuring the Web, draw on creative
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and topical expertise. These three applications are bundled
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into one Android package, and each can be accessed
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interchangeably through Twitch's settings menu.
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=== Module 1: Census ===
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==== Problem/Limitations ====
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Despite progress in producing effective understanding of
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static elements of our physical world — routes, businesses
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and points of interest — we lack an understanding of
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human activity. How busy is the corner cafe at 2pm on
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Fridays? What time of day do businesspeople clear out of
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the downtown district and get replaced by socializers?
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Which neighborhoods keep high-energy activities going
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until 11pm, and which ones become sleepy by 6pm? Users
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could take advantage of this information to plan their
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commutes, their social lives and their work.
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==== Module preview ====
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Also, after completing the task, the requester has to rate the worker and that ratings will be saved along with the task category.
Existing crowdsourced techniques such as Foursquare are
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too sparse to answer these kinds of questions: the answers
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require at-the-moment, distributed human knowledge. We
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envision that twitch crowdsourcing can help create a
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human-centered equivalent of Google Street View, where a
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user could browse typical crowd activity in an area. To do
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so, we ask users to answer one of several questions about the world around them each time they unlock their phone.
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Users can then browse the map they are helping create.
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==== System details ====
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So, when another requester post a similar task, the system looks for a suitable workers that has the highest rating and the category the worker most familiar with.
Census is the default crowdsourcing task in Twitch. It
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collects structured information about what people
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experience around them. Each Census unlock screen
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consists of four to six tiles (Figures 1 and 3), each task
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centered around questions such as:
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• How many people are around you?
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• What kinds of attire are nearby people wearing?
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• What are you currently doing?
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• How much energy do you have right now?
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While not exhaustive, these questions cover several types of
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information that a local census might seek to provide. Two
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of the four questions ask users about the people around
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them, while the other two ask about users themselves; both
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of which they are uniquely equipped to answer. Each
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answer is represented graphically; for example, in case of
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activities, users have icons for working, at home, eating,
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travelling, socializing, or exercising.
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To motivate continued engagement, Census provides two
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modes of feedback. Instant feedback (Figure 4) is a brief
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Android popup message that appears immediately after the
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user makes a selection. It reports the percentage of
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responses in the current time bin and location that agreed
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with the user, then fades out within two seconds. It is
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transparent to user input, so the user can begin interacting
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with the phone even while it is visible. Aggregated report
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allows Twitch users to see the cumulative effect of all
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users’ behavior. The data is bucketed and visualized on a
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map (Figure 2) on the Twitch homepage. Users can filter
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the data based on activity type or time of day.
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Revision as of 13:18, 13 February 2016

System

Introducing the workers skills to the Boomerang Ranking

Brief introduction of the system

Boomerang, the rating system that uses in Daemo decide what are tasks that workers going to get in their future and what type of workers, requesters going to get for their task. Requesters' ratings of workers of tasks are used to give early access to workers that requester rates highly. However, Boomerang still can recommend a worker to the requester who are not familiar with the posted task because Boomerang doesn't consider the workers skill level related to that particular task when recommend.

For example:-

  • Requester post two tasks to the platform (Image tagging, Translation)
  • When the requester posts the tasks Boomerang recommend the higher rating workers first
  • However, among those workers, there can be workers who are not familiar with those tasks because those workers previously have worked with the requester on some other type of tasks( Surveys, Bookkeeping, etc.)
  • So, if the requester selects one of the higher rating worker not familiar with the task, there can be a quality issue. To mitigate the risk we proposed to introduce a task category filter option to Boomerang system.

How is the system solving critical problems

When a requester create a task he should choose a category relevant to that particular task. For example, if it's a image tagging task, task should go under 'Image tagging' category etc. So when the requester post this task to Crowd sourced market place, The system choose highly rated workers first and filter them according the category they have rated. Then Boomerang recommend the workers who has higher rating in that particular category. That way, the requester will get the most suitable higher rated workers first.

Also, after completing the task, the requester has to rate the worker and that ratings will be saved along with the task category.

So, when another requester post a similar task, the system looks for a suitable workers that has the highest rating and the category the worker most familiar with.