TY - JOUR
T1 - A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
AU - Kamel, Menna Maged
AU - Gil-Solla, Alberto
AU - Guerrero-Vásquez, Luis Fernando
AU - Blanco-Fernández, Yolanda
AU - Pazos-Arias, José Juan
AU - López-Nores, Martín
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10
Y1 - 2023/10
N2 - Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.
AB - Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.
KW - classification
KW - crowdsourcing
KW - cultural heritage
KW - ontology
KW - recommendation system
KW - word embeddings
UR - http://www.scopus.com/inward/record.url?scp=85174156964&partnerID=8YFLogxK
U2 - 10.3390/app131910623
DO - 10.3390/app131910623
M3 - Article
AN - SCOPUS:85174156964
SN - 2076-3417
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 19
M1 - 10623
ER -