The article discusses the problem of relevance ranking – sorting objects according to a given criterion. This is a challenge because different users may prefer different criteria, so the ranking algorithm needs to adapt to user needs. The authors present SortNet, an adaptive ranking algorithm that orders objects using a neural network as a comparator. The neural network training set provides examples of the desired ordering between pairs of items. The comparator adopts a connectionist architecture that is particularly suited for implementing a preference function. The authors prove that this architecture has the universal approximation property and can implement a wide class of functions.
Publication date: 6 Nov 2023
Project Page: Not provided
Paper: https://arxiv.org/pdf/2311.01864
