1. A method comprising: deriving a listwise loss function from one or more sample listwise rankings, individual listwise rankings including individual items ordered based on the relevancy of the individual items, with respect to an exemplary listwise ranking; and using a computing device, ranking a plurality of items using the listwise loss function, wherein the listwise loss function is: L ⁡ ( y ( i ) , z ( i ) ⁡ ( f ω ) ) = - ∑ ∀ g ∈ G k ⁢ P y ( i ) ⁡ ( g ) ⁢ log ⁡ ( P z ( i ) ⁡ ( f ω ) ⁡ ( g ) ) ; in which: y (i) is a judgement value for an item with respect to a query; P y (i) is a probability of y (i) ; z (i) is a list of scores for y (i) with a neural network model function ƒ ω applied to individual y (i) according to an equation z ( i ) = ( f ⁡ ( x 1 ( i ) ) , … ⁢ , f ⁡ ( x n ( i ) ( i ) ) ) ; P z (i) (ƒ ω ) is a probability of z (i) ; and - ∑ ∀ g ∈ G k ⁢ P y ( i ) ⁡ ( g ) ⁢ log ⁡ ( P z ( i ) ⁡ ( f ω ) ⁡ ( g ) ) is a cross entropy for the query “q (i) ” in which G k is a top k subgroup for the query and g is an element of collection G k .