Adaptive pointwise-pairwise learning to rank

Abstract

A method of ranking items for a given entity uses sets of triplets <u, i, j>, each set of triplets including an entity u and a pair of items i and j with a known relative relevance for entity u, to train a learnable scoring function ƒ and to learn optimized values of a first set θ of learnable parameters. The training includes optimizing a loss function depending on θ, on a second set of learnable parameters θ, and on a probability of having the item i preferred to the item j by the entity u. The probability defines a continuum between pointwise and pairwise ranking of items through a learnable mixing function depending on θ. After training, the trained learnable scoring function ƒ is applied to all input pairs <u', i′> to rank all items i′ for an entity u′.