How to understand the input_field and out_field in this sample?

Amazon KDD Cup 2024: Multi-Task Online Shopping Ch
#1

The input_field prompt said ‘You should output a permutation of 1 to 5. There should be a comma separating two numbers. Each product and its number should appear only once in the output.’. But the out_field is [1,0.01,0.1,1,0]. This confused me.

sample:
{“input_field”:"A user has made a query with keyword ‘jeep liberty lift’. Given the following numbered list of 5 products, please rank the products according their relevance with the query. \nProduct List: \n1. Supreme Suspensions - Front Leveling Kit for 2002-2007 Jeep Liberty KJ and 2008-2012 Jeep Liberty KK 2.5" Front Lift High-Strength Carbon Steel Strut Spacers 2WD 4WD\n2. Rough Country 2.5" Lift Kit for 2007-2018 Jeep Wrangler JK 4DR - 67930\n3. Rough Country 2.5" Lift Kit (fits) 1997-2006 Jeep Wrangler TJ LJ | 6 CYL | N3 Shocks | Suspension System | 653.20\n4. Supreme Suspensions - Full Lift Kit for 2008-2012 Jeep Liberty KK 2.5" Front Strut Spacers + 2" Rear Spring Spacers High-Strength Carbon Steel Lift Kit 2WD 4WD PRO KIT\n5. TeraFlex 1251000 2.5" Lift Kit (JK 4 Door with All (4) 2.5" Shock)\nYou should output a permutation of 1 to 5. There should be a comma separating two numbers. Each product and its number should appear only once in the output. Only respond with the ranking results. Do not say any word or explanations.\nOutput: ",“output_field”:[1,0.01,0.1,1,0],“task_name”:“task12”,“task_type”:“ranking”,“metric”:“ndcg”,“is_multiple_choice”:false,“track”:“amazon-kdd-cup-24-user-behavior-alignment”}

#2

This output_field refers to the matching weights (or relevance) of each product with the query. The weight/relevance will be used to calculate NDCG (Ref here)

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#3

I have the same question. Our doubt is not about the meaning of ‘the matching weights,’ but rather how they are generated. Is this related to Amazon’s business system? At first, we thought the weights were obtained by smoothing the CTR of the items corresponding to the query. However, we have observed that the output values are only 1, 0.01, 0.1, and 0, which is not quite consistent with our assumption. We hope you can help clarify this issue.

#4

So basically they are generated by first identifying four types of query-product relations. Then, each type of relations is given a relevance, where ‘perfect match’ is 1 and ‘irrelevant’ is 0.