Tourist Attraction Recommendation Based on Knowledge Graph - Intelligent Information Processing IX Access content directly
Conference Papers Year : 2018

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.
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hal-02197799 , version 1 (30-07-2019)

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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⟩
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