A contrastive neural disentanglement approach for query performance prediction

Sara Salamat, Negar Arabzadeh, Shirin Seyedsalehi, Amin Bigdeli, Morteza Zihayat, Ebrahim Bagheri
January 2025

Abstract

This paper presents a contrastive neural disentanglement approach for query performance prediction, improving accuracy and interpretability of QPP systems.

Type
Journal article
Publication
Machine Learning

This work introduces contrastive neural disentanglement for query performance prediction.

Contrastive Learning Neural Disentanglement Query Performance Prediction Machine Learning Information Retrieval
Morteza Zihayat
Morteza Zihayat
Principal Investigator

Dr. Morteza Zihayat is an Assistant Professor in the School of Information Technology Management at Toronto Metropolitan University and the Director of the Human-Centered Machine Intelligence Lab.