Exploiting available domain knowledge to improve the retrieval and recommendation of Digital Cultural Heritage materials
type: CreativeWork
description: Cultural Heritage (CH) institutions, such as museums, have recently embraced computing techniques to digitise CH materials (artefacts, paintings, books etc) and to make accessible those digital representations through their online portals to millions of museum visitors (onsite and remote). This mass availability of digitised materials, however, can lead to information overload. Therefore, ordinary CH online users can find it challenging to access these materials, because they usually have no domain knowledge and also lack the experience of which precise keyword terms to use to search and discover new information. As an attempt to mitigate the issues explained above, recommender systems and visual search interfaces have been used by millions of users to discover new and relevant to the users’ interests CH materials. A CH recommender system is a system that uses knowledge — content and social — representations assembled from various domain knowledge sources, to generate personalised recommendations of CH materials. Social knowledge representations provide better recommendation quality than content knowledge representations when they have substantial social knowledge such as user-interactions and social tagging in the representation, but they suffer when available information is insufficient (cold start problem and sparsity of social knowledge). Different approaches have been deployed to address these challenges, for example a hybrid approach that incorporated content directly into a social knowledge representation to provide a recommendation. But this hybrid approach only works well on domains for which specific content knowledge exists which can directly describe an item and is always available and meaningful. The CH domain does not have such rich specific knowledge that can directly describe the content of CH materials, thus limiting the ability to incorporate content directly into the social knowledge representation for CH recommendations.To address these challenge…
abstract: Cultural Heritage (CH) institutions, such as museums, have recently embraced computing techniques to digitise CH materials (artefacts, paintings, books etc) and to make accessible those digital representations through their online portals to millions of museum visitors (onsite and remote). This mass availability of digitised materials, however, can lead to information overload. Therefore, ordinary CH online users can find it challenging to access these materials, because they usually have no domain knowledge and also lack the experience of which precise keyword terms to use to search and discover new information. As an attempt to mitigate the issues explained above, recommender systems and visual search interfaces have been used by millions of users to discover new and relevant to the users’ interests CH materials. A CH recommender system is a system that uses knowledge — content and social — representations assembled from various domain knowledge sources, to generate personalised recommendations of CH materials. Social knowledge representations provide better recommendation quality than content knowledge representations when they have substantial social knowledge such as user-interactions and social tagging in the representation, but they suffer when available information is insufficient (cold start problem and sparsity of social knowledge). Different approaches have been deployed to address these challenges, for example a hybrid approach that incorporated content directly into a social knowledge representation to provide a recommendation. But this hybrid approach only works well on domains for which specific content knowledge exists which can directly describe an item and is always available and meaningful. The CH domain does not have such rich specific knowledge that can directly describe the content of CH materials, thus limiting the ability to incorporate content directly into the social knowledge representation for CH recommendations.To address these challenge…
author: Usman, MA
language: en
date published: 2021
date modified: 2026-07-24T04:26:12Z
provider: U. of Salford
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: U. of Salford