Query Performance Prediction: From Fundamentals to Advanced Techniques

Negar Arabzadeh, Chuan Meng, Mohammad Aliannejadi, Ebrahim Bagheri

Abstract: Query performance prediction (QPP) is a core task in information retrieval (IR) that aims at predicting the retrieval quality for a given query without relevance judgments. QPP has been investigated for decades and has witnessed a surge in research activity in recent years; QPP has been shown to benefit various aspects, e.g., improving retrieval effectiveness by selecting the most effective ranking function per query [5, 7]. Despite its importance, there is no recent tutorial to provide a comprehensive overview of QPP techniques in the era of pre-trained/large language models or in the scenario of emerging conversational search (CS); In this tutorial, we have three main objectives. First, we aim to disseminate the latest advancements in QPP to the IR community. Second, we go beyond investigating QPP in ad-hoc search and cover QPP for CS. Third, the tutorial offers a unique opportunity to bridge the gap between theory and practice; we aim to equip participants with the essential skills and insights needed to navigate the evolving landscape of QPP, ultimately benefiting both researchers and practitioners in the field of IR and encouraging them to work around the future avenues on QPP.