Clustering helps to improve price prediction in online booking systems

Abstract

Purpose

Pricing on the online booking systems is a difficult task for the host, the systems usually set the prices that are lower than the general premises and quality, and that only gives benefits to the system by easily attracting the customer to use the service. The setting price of the new accommodation is often based on location, the number of beds, type of house and so on. The main problem is to predict the most reasonable price for the host. This paper aims to study the use of machine learning and sentiment analysis for predicting the price of online booking systems.

Design/methodology/approach

In particular, an empirical study is performed first for some well-known classification models for the problems. The authors then propose to apply k-means, a clustering technique, together with Gradient Boost and XGBoost models to improve the prediction performance. Experiments are conducted and tested for real Airbnb data sets collected in London City.

Findings

Experimental results are given and compared to show that the authors’ method outperforms to an updated method.

Originality/value

The authors use k-means and sampling together with Gradient Boost and XGBoost models to improve the prediction performance.

Citation

Trang, L.H., Huy, T.D. and Le, A.N. (2021), "Clustering helps to improve price prediction in online booking systems", International Journal of Web Information Systems, Vol. 17 No. 1, pp. 45-53. https://doi.org/10.1108/IJWIS-11-2020-0065