Title: TEPM: traveller enrolment prediction mechanism using BERT-based feature clustering and LSTM models

Authors: Chung-You Tsai; Ming-Yang Su; Christopher Chuang; Chih-Yung Chang; Diptendu Sinha Roy

Addresses: Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City, 220216, Taiwan; Department of Electrical Engineering, Yuan Ze University, Taoyuan City, 320, Taiwan ' Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan City, 333, Taiwan ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, 25137, Taiwan ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, 25137, Taiwan ' Department of Computer Science and Engineering, National Institute of Technology, Shillong, 793003, India

Abstract: 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.

Keywords: tour group prediction; feature clustering; natural language processing; BERT model; LSTM enrolment prediction.

DOI: 10.1504/IJAHUC.2024.138748

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.46 No.1, pp.14 - 26

Received: 08 Nov 2023
Accepted: 04 Dec 2023
Published online: 29 May 2024 *

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