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User Simulation for Evaluating Information Access Systems.

Authors :
Balog, Krisztian
Zhai, ChengXiang
Source :
Foundations & Trends in Information Retrieval; 2024, Vol. 18 Issue 1/2, p1-261, 261p
Publication Year :
2024

Abstract

Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge is rooted in the difficulty of assessing a system's overall effectiveness in assisting users to complete tasks through interactive support, and further exacerbated by the substantial variation in user behaviour and preferences. To address this challenge, user simulation emerges as a promising solution. This monograph focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We begin with a background of information access system evaluation and explore the diverse applications of user simulation. Subsequently, we systematically review the major research progress in user simulation, covering both general frameworks for designing user simulators, utilizing user simulation for evaluation, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. Realizing that user simulation is an interdisciplinary research topic, whenever possible, we attempt to establish connections with related fields, including machine learning, dialogue systems, user modeling, and economics. We end the monograph with a broad discussion of important future research directions, many of which extend beyond the evaluation of information access systems and are expected to have broader impact on how to evaluate interactive intelligent systems in general. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15540677
Volume :
18
Issue :
1/2
Database :
Complementary Index
Journal :
Foundations & Trends in Information Retrieval
Publication Type :
Academic Journal
Accession number :
177887360
Full Text :
https://doi.org/10.1561/1500000098