A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management

Yuanyuan Zhuang,Jaekyeong Kim

doi:10.3390/su13148039

Yuanyuan Zhuang, Jaekyeong Kim

Open Access

https://doi.org/10.3390/su13148039

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Journal: Sustainability	Publication Date: Jul 19, 2021
Citations: 18	License type: CC BY 4.0

Affiliation: Kyung Hee University

Abstract

Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not consider various aspects of hotels well. Therefore, this study performs fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model using review data with rating labels on the TripAdvisor site. This study suggests a multi-criteria recommender system to recommend a suitable target customers for the hotel. As the rating values of six criteria of TripAdvisor are insufficient, the proposed recommender system uses fine-tuned BERT to predict six criteria ratings. Based on this predicted ratings, a multi-criteria recommender system recommends personalized Top-N customers for each hotel. The performance of the multi-criteria recommender system suggested in this study is better than that of the benchmark system, a single-criteria recommender system using overall ratings.

Highlights

IntroductionOnline travel agencies have numerous hotels registered, and numerous reviews are posted every day, resulting in information overload, which puts pressure on customers to make a choice
The tourism industry has brought about many changes due to the development of information technology and the Internet
The review data set is divided into a training set, a test set and a development set at a ratio of 8:1:1

Summary

Introduction

Online travel agencies have numerous hotels registered, and numerous reviews are posted every day, resulting in information overload, which puts pressure on customers to make a choice. To solve these problems and provide customers with better services, hotel recommender systems have been introduced by major travel agencies, thereby reducing the user’s decision-making time and efforts [2]. From the point of view of the hotel, it is necessary to accurately identify and promote to the customers who may visit the hotel. From the customer’s point of view, rather than receiving promotions from numerous hotels, they want to be recommended only from hotels that are appropriate for the customer. Through personalized recommendation, it is possible to effectively promote the hotel through the recommendation of available customers at the hotel, as well as to increase the customer’s order rate and to help increase the recognition and credibility of the hotel

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Conclusion