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Human-AI Interaction for Exploratory...

Lee, Benjamin Charles Germain,

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Human-AI Interaction for Exploratory Search & Recommender Systems With Application to Cultural Heritage /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human-AI Interaction for Exploratory Search & Recommender Systems With Application to Cultural Heritage // Benjamin Charles Germain Lee.
Author:	Lee, Benjamin Charles Germain,
Description:	1 electronic resource (294 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Information science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30526266
ISBN:	9798379909635

Human-AI Interaction for Exploratory Search & Recommender Systems With Application to Cultural Heritage /
Lee, Benjamin Charles Germain,

Human-AI Interaction for Exploratory Search & Recommender Systems With Application to Cultural Heritage /Benjamin Charles Germain Lee. - 1 electronic resource (294 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Exploratory search and recommender systems are ubiquitous and central to information navigation. Yet, many pressing challenges remain surrounding the development of robust systems, from producing high-quality data and metadata to answering fundamental questions in human-AI interaction concerning the interactive affordances for search and recommendation. These challenges are exacerbated by 1) the ever-expanding wealth of information to be searched, and 2) the widespread incorporation of increasingly opaque and complex machine learning models into deployed systems. This thesis explores these challenges and investigates how we can improve interaction mechanisms in exploratory search and recommendation. Much of this dissertation adopts the setting of digital cultural heritage collections, where impoverished metadata redoubles challenges of searchability, with implications across disciplines.This dissertation introduces three primary contributions through publicly deployed systems and datasets. First, we demonstrate how the construction of large-scale cultural heritage datasets using machine learning can answer interdisciplinary questions in library & information science and the humanities (Chapter 2). Second, based on the feedback of users of these cultural heritage datasets, we introduce open faceted search, an extension of faceted search that leverages human-AI interaction affordances to empower users to define their own facets in an open domain fashion (Chapter 3). Third, encountering similar challenges with the deluge of scientific papers, we explore the question of how to improve recommender systems through human-AI interaction and tackle the broad challenge of advice taking for opaque machine learners.

English

ISBN: 9798379909635Subjects--Topical Terms:

554358
Information science.
Subjects--Index Terms:

Computing cultural heritage

Human-AI Interaction for Exploratory Search & Recommender Systems With Application to Cultural Heritage /
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