HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Tourist Attraction Recommendation Based on Knowledge Graph

Abstract : This paper focuses on building recommendation model based on knowledge graph in the tourism field. A knowledge graph for tourist attractions in the Bangkok city is constructed, and a tourist attraction recommendation model based on the knowledge graph is presented. Firstly, we collect tourism data in Bangkok and generate a tourist attraction knowledge graph by using the Neo4j tool. Then, by applying the network representation learning method Node2Vec, we generate the feature vectors of both attractions and tourists, and calculate the correlation scores between tourists and attractions according to the cosine similarity. Finally, we normalize the correlation scores to generate the recommended list. This model presented in the paper can overcome the sparsity problem of tourist knowledge graphs and can be used in large scale knowledge graph.
Document type :
Conference papers
Complete list of metadata

Cited literature [9 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Tuesday, July 30, 2019 - 5:02:12 PM
Last modification on : Tuesday, July 30, 2019 - 5:12:17 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Phatpicha Yochum, Liang Chang, Tianlong Gu, Manli Zhu, Weitao Zhang. Tourist Attraction Recommendation Based on Knowledge Graph. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.80-85, ⟨10.1007/978-3-030-00828-4_9⟩. ⟨hal-02197799⟩



Record views


Files downloads