TEPM: Traveler Enrollment Prediction Mechanism using BERT-based Feature Clustering and LSTM Models
學年 112
學期 2
出版（發表）日期 2024-05-29
作品名稱 TEPM: Traveler Enrollment Prediction Mechanism using BERT-based Feature Clustering and LSTM Models
作品名稱（其他語言）
著者 Chung-You Tsai; Ming-Yang Su; Christopher Chuang; Chih-Yung Chang; Diptendu Sinha Roy
單位
出版者
著錄名稱、卷期、頁數 International Journal of Ad Hoc and Ubiquitous Computing , vol. 46, no. 1, pp. 14-26
摘要 The prediction of whether a tour group will form or not has a significant impact on travellers' future itinerary planning and travel agencies' control over hotel and flight bookings. Traditional methods rely solely on historical data, therefore lacks accuracy due to diverse tour attributes. The proposed mechanism, called TEPM, divides the enrolment prediction into three stages, including clustering, classification and prediction. Firstly, it clusters the tours to several groups according to the enrolment data. Secondly, natural language processing techniques are used to convert tour advertisements into feature documents. The BERT is employed to learn the relationship between advertisement feature documents and clusters. This enables the prediction of the group to which a given tour advertisement belongs. Finally, in the prediction stage, this paper employs dedicated LSTM models for each cluster to predict the number of enrolees. Experiments show that this approach performs well in terms of precision, recall, and F1 score.
關鍵字 tour group prediction; feature clustering; natural language processing; BERT model; LSTM enrolment prediction
語言 en
ISSN 1743-8233
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126325 )

SDGS 尊嚴就業與經濟發展,產業創新與基礎設施