Listwise Explanations for Ranking Models Using Multiple Explainers

Lijun Lyu*, Avishek Anand

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
30 Downloads (Pure)

Abstract

This paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a Generalized Preference Coverage (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method Multiplex uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers’ practical utility.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings
EditorsJaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Annalina Caputo, Udo Kruschwitz
Place of PublicationCham
PublisherSpringer
Pages653-668
Number of pages16
ISBN (Electronic)978-3-031-28244-7
ISBN (Print)978-3-031-28243-0
DOIs
Publication statusPublished - 2023
Event45th European Conference on Information Retrieval, ECIR 2023 - Dublin, Ireland
Duration: 2 Apr 20236 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume13980
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference45th European Conference on Information Retrieval, ECIR 2023
Country/TerritoryIreland
CityDublin
Period2/04/236/04/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Explanation
  • List-wise
  • Neural
  • Post-hoc
  • Ranking

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