Publication at Faculty of Mathematics and Physics |
2023
Abstract
Popular travel-related forums provide prodigious support to customers. In this paper, we inspect the opportunities to amplify the power of recommender systems with the principles of social networks and advanced machine learning.
When exploring the Tripadvisor data, the hotels and review authors are considered to be the nodes of a social networks whereas the awarded ratings represent edges. Supporting experiments confirm applicability of graph neural networks to model travel-related data despite disregarding textual or image-related parts of the reviews.