For my clients, the standard use cases for e-commerce.
For example, you might have 100,000 products plus another 5,000 new ones listed per month. Your human error rate might be 3%. When a product arrives, its buyer (the corporate person responsible for signing on the brand) gives it a category and it shows up on the site. So 3% of the new products are erroneously categorised by their buyer.
You give X human votes per product, say, 10. "Which of these categories applies to this product?"
If the votes agree with each other, you can just input that as the category. If there's a split, you can feed those products to a more open ended question like "what category is this product" and use that as a starting point for either renaming the categories, finding a new category, or tagging: if it's a 50/50 split, you can just tag the product with both categories.
This is all automated and used to cost me around $200 per 5,000 products.
Another example might be training a data set for a machine learning algorithm. You send a data set to be trained by Turk and use it as training/test sets. I'm keeping this one deliberately vague for now as AFAIK we are the only ones in our space doing this and I don't even want to mention the space due to a relatively smart competitor.
As a hypothetical example, you might be trying to predict the category from the description, gender, picture features, buyer and brand, and the above categorisation tasks can be used to train the algorithms you're testing out.
For example, you might have 100,000 products plus another 5,000 new ones listed per month. Your human error rate might be 3%. When a product arrives, its buyer (the corporate person responsible for signing on the brand) gives it a category and it shows up on the site. So 3% of the new products are erroneously categorised by their buyer.
You give X human votes per product, say, 10. "Which of these categories applies to this product?"
If the votes agree with each other, you can just input that as the category. If there's a split, you can feed those products to a more open ended question like "what category is this product" and use that as a starting point for either renaming the categories, finding a new category, or tagging: if it's a 50/50 split, you can just tag the product with both categories.
This is all automated and used to cost me around $200 per 5,000 products.
Another example might be training a data set for a machine learning algorithm. You send a data set to be trained by Turk and use it as training/test sets. I'm keeping this one deliberately vague for now as AFAIK we are the only ones in our space doing this and I don't even want to mention the space due to a relatively smart competitor.
As a hypothetical example, you might be trying to predict the category from the description, gender, picture features, buyer and brand, and the above categorisation tasks can be used to train the algorithms you're testing out.