SortNet: learning to rank by a neural-based sorting algorithm

The problem of relevance ranking consists of sorting a set of objects with respect to a given criterion. Since users may prefer different relevance criteria, the ranking algorithms should be adaptable to the user needs. Two main approaches exist in literature for the task of learning to rank: 1) a score function, learned by examples, which evaluates the proper- ties of each object yielding an absolute relevance value that can be used to order the objects or 2) a pairwise approach, where a “preference function” is learned using pairs of ob- jects to define which one has to be ranked first. In this paper, we present SortNet, an adaptive ranking algorithm which orders objects using a neural network as a comparator. The neural network training set provides examples of the de- sired ordering between pairs of items and it is constructed by an iterative procedure which, at each iteration, adds the most informative training examples. Moreover, the com- parator adopts a connectionist architecture that is particu- larly suited for implementing a preference function. We also prove that such an architecture has the universal approxima- tion property and can implement a wide class of functions. Finally, the proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state of the art algorithms.