Yes, I am talking about the Food Preparation time that @zomato displays on their app after the user orders food.
I ordered a pizza from @zomato recently. I thought this feature was a simple implementation, but this shocked me when I got to know the reality.
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Yes, I am talking about the Food Preparation time that @zomato displays on their app after the user orders food.
I was amazed after knowing this, and here is how they implement this to achieve accurate results.
But more factors are in play here like queued orders, fine dining restaurants vs delivery kitchens, opening hours, etc.
These categories are encoded using Word2Vec Embedding and Categorical Embedding, respectively.
Concurrently they passed both running orders and completed orders through a stacked LSTM layer.
The resulting vector after concatenation is pass through a two-layer dense network to get food preparation time.