Paper Contents
Abstract
Objective"Search result diversification aims to retrieve diverse results to satisfy as manydifferent information needs as possible"AbstractionSupervised methods have been proposed recently to learn rankingfunctions and they have been shown to produce superior results to unsupervisedmethods.However, these methods use implicit approaches based on the principle of MaximalMarginal Relevance (MMR).In this paper, we propose a learning framework for explicit result diversificationwhere subtopics are explicitly modeled.Based on the information contained in the sequence of selected documents, we useattention mechanism to capture the subtopics to be focused on while selecting thenext document, which naturally fits our task of document selection fordiversification.
Copyright
Copyright © 2025 MAJJI MAHISHREE. This is an open access article distributed under the Creative Commons Attribution License.
