New hybrid semantic-based collaborative filtering recommender systems

Alhijawi, Bushra; Obeid, Nadim; Awajan, Arafat; Tedmori, Sara

doi:10.1007/s41870-022-01011-x

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…The system recommends art-related concepts based on user ratings of artwork and concepts from the underlying ontology used to model the museum's collections. Recommender systems empowered using semantic technologies have also been developed in other domains such as e-commerce [23], e-learning [24], and job recruitment [25].…”

Section: Semantic Recommendationmentioning

confidence: 99%

Towards a Semantics-Based Recommendation System for Cultural Heritage Collections

Bikakis

2023

Applied Sciences

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While the use of semantic technologies is now commonplace in the cultural heritage sector and several semantically annotated cultural heritage datasets are publicly available, there are few examples of cultural portals that exploit these datasets and technologies to improve the experience of visitors to their online collections. Aiming to address this gap, this paper explores methods for semantics-based recommendations aimed at visitors to cultural portals who want to explore online collections. The proposed methods exploit the rich semantic metadata in a cultural heritage dataset and the capabilities of a graph database system to improve the accuracy of searches through the collection and the quality of the recommendations provided to the user. The methods were developed and tested with the Archive of the Art Textbooks of Elementary and Public Schools in the Japanese Colonial Period. However, they can easily be adapted to any cultural heritage collection dataset modelled in RDF.

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Section: Semantic Recommendationmentioning

confidence: 99%