Online platforms, serving as the primary conduit for travelers to seek, compare, and secure travel accommodations, require a profound understanding of user dynamics to craft competitive and enticing offerings. Concurrently, recent advancements in Natural Language Processing, particularly large language models, have made substantial strides in capturing the complexity of human language. Simultaneously, knowledge graphs have become a formidable instrument for structuring and categorizing information. This paper introduces a cutting-edge deep learning methodology integrating large language models with domain-specific knowledge graphs to classify tourism offers. It aims at aiding hospitality operators in understanding their accommodation offerings’ market positioning, taking into account the visit propensity and user review ratings, with the goal of optimizing the offers themselves and enhancing their appeal. Comparative analysis against alternative methods on two datasets of London accommodation offers attests to our approach’s effectiveness, demonstrating superior results.

Cadeddu, A., Chessa, A., De Leo, V., Fenu, G., Motta, E., Osborne, F., et al. (2023). Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain. In Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2023) co-located with the 21th International Semantic Web Conference (ISWC 2023). CEUR-WS.

Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain

Osborne F.;
2023

Abstract

Online platforms, serving as the primary conduit for travelers to seek, compare, and secure travel accommodations, require a profound understanding of user dynamics to craft competitive and enticing offerings. Concurrently, recent advancements in Natural Language Processing, particularly large language models, have made substantial strides in capturing the complexity of human language. Simultaneously, knowledge graphs have become a formidable instrument for structuring and categorizing information. This paper introduces a cutting-edge deep learning methodology integrating large language models with domain-specific knowledge graphs to classify tourism offers. It aims at aiding hospitality operators in understanding their accommodation offerings’ market positioning, taking into account the visit propensity and user review ratings, with the goal of optimizing the offers themselves and enhancing their appeal. Comparative analysis against alternative methods on two datasets of London accommodation offers attests to our approach’s effectiveness, demonstrating superior results.
paper
BERT; Classification Tasks; Feature Engineering; Hospitality; Knowledge Graphs; Natural Language Processing; Tourism;
English
2023 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2023 - November 6-10, 2023
2023
Alam, M; Buscaldi, D; Cochez, M; Osborne, F; Reforgiato Recupero, D
Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2023) co-located with the 21th International Semantic Web Conference (ISWC 2023)
2023
3559
https://ceur-ws.org/Vol-3559/
open
Cadeddu, A., Chessa, A., De Leo, V., Fenu, G., Motta, E., Osborne, F., et al. (2023). Leveraging Knowledge Graphs with Large Language Models for Classification Tasks in the Tourism Domain. In Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2023) co-located with the 21th International Semantic Web Conference (ISWC 2023). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/455379
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