Difference between revisions of "Milestone 4 TuringMachine: Sustainable Reputation Mechanism; Insights from Finance Industry"
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===Latent Factor Model for Workers to
===Latent Factor Model for Workers to Recommendation ===
Revision as of 22:25, 25 March 2015
Influence and Related work
The Future of Work, Kittur etal 2013 and Genomes of Collective Intelligence Framework, Malone etal 2010 have shown that Reputation plays a big role in developing sustainable crowd sourcing communities. There is huge risk involve in the crowd sourcing work. Both requestors and workers are exposed to the risk related to payment or quality. We build our idea based on theories in structured finance and recommendation system.
- Workers and requestors rate each other.
- An automated tranche creation algorithm clusters workers into A to E category.
Tranches Structure Penalties, Incentives, Default Risk
- For the requestors in A tranche the minimum wage requirement is avg/below avg. However, for the requestors in D the minimum wage requirement is higher because he has a greater risk of default. Similar structure is designed for the worker. Please see figure 2.
- Based on the ratings we can create the tranches of workers and requestors
- This structure exposes the risk involved in the crowdsourcing platforms.
- The workers can build the task portfolio using rating profiles and maximize the profit by diversifying the risk. For instance, a worker can select one task from tranch A, 3 from Avg, and rest from D.
Automated Worker-Requestor Recommendations
- We combine Recommendation engine with Tranches and automatically match up requestors and workers.
Combineing Global and Local Effects
- Combine baseline method with latent factor model
Latent Factor Model for Workers to Requestors Recommendation
- Recommend workers to requestors