Abstract：Human-AI Interaction for Exploratory SearchRecommender Systems With Application to Cultural Heritage Exploratory search and recommender systems are ubiquitous and central to information navigationYetmany pressing challenges remain surrounding the development of robust systemsfrom producing high-quality data and metadata to answering fundamental questions in human-AI interaction concerning the interactive affordances for search and recommendationThese challenges are exacerbated by 1the ever-expanding wealth of information to be searchedand 2the widespread incorporation of increasingly opaque and complex machine learning models into deployed systemsThis thesis explores these challenges and investigates how we can improve interaction mechanisms in exploratory search and recommendationMuch of this dissertation adopts the setting of digital cultural heritage collectionswhere impoverished metadata redoubles challenges of searchabilitywith implications across disciplinesThis dissertation introduces three primary contributions through publicly deployed systems and datasetsFirstwe demonstrate how the construction of large-scale cultural heritage datasets using machine learning can answer interdisciplinary questions in libraryinformation science and the humanitiesChapter 2Secondbased on the feedback of users of these cultural heritage datasetswe introduce open faceted searchan extension of faceted search that leverages human-AI interaction affordances to empower users to define their own facets in an open domain fashionChapter 3Thirdencountering similar challenges with the deluge of scientific paperswe explore the question of how to improve recommender systems through human-AI interaction and tackle the broad challenge of advice taking for opaque machine learnersLibrary AutomationCreate large-scale cultural heritage data sets through machine learningRetrieval and IndexingThe open-oriented search here is a retrieval tool that allows users to define their search aspectsConsulting ServiceResearch and explore how to improve the recommendation system through human-AI interactionwhich involves suggestions for usersMachine LearningThis paper involves using machine learning to establish large-scale cultural heritage data sets and discuss how to improve the recommendation systemExpert SystemThe paper introduces open multi-oriented searchThis is an expansion that allows users to define their own multi-directional search in the open fieldIt uses artificial intelligence interaction functions