| Issue |
ITM Web Conf.
Volume 48, 2022
The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 5 | |
| Section | Computer Science, Intelligent Systems and Information Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20224803001 | |
| Published online | 02 September 2022 | |
A framework for recommending tourist attractions using deep learning and association rule mining-based methods
Mokpo National University, Department of Software Convergence Engineering, 1666 Yeong san-ro, Cheonggye-myeon, Muan-gun, Jeollanam-do, 58554, Korea
* Corresponding author: hanjojeong@mnu.ac.kr
Abstract. Many of tourism recommendation researches are based on the user rating and review data on the tourism platforms, and these approaches might be only suitable for a discrete recommendation for the tourist attractions. It is because each rating and review data on the platforms is created for a tourist place, not for multiple places on a travel itinerary. A travel blog data often contains information about the multiple places on a travel itinerary, but it is difficult to analyse the data compared to the rating and review data since it is like a text document having longer text than the review. In this paper, we introduce a framework consisting of a deep learning-based tourist-attraction extraction method from the blog text and an association rule mining-based recommendation method to recommend a list of tourist attractions that might be favourable to visit together in a travel itinerary.
© The Authors, published by EDP Sciences, 2022
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