Recommending ancillary products in aviation industry: A comparative study on recommender systems using online customer reviews

Increasing competition in the aviation industry forces airline companies to find new ways to increase their profitability. Airline companies try to do that by offering ancillary products beside their main service. However, there is the problem of offering a suitable product to the customer who really needs it. Recent improvements in the recommender systems area, especially in the e-commerce industry, raise the question of whether those systems are efficient for recommending ancillary products in aviation industry. In this study we aim to build a recommender system for ancillary products for the airline industry using online customer reviews. Customer reviews from various web sites were separated by their topics using BERTOPIC topic modelling algorithm. Expert labeled customer reviews were fed into the algorithms to build recommender system. The aim of the study is to compare recommender systems using different machine learning algorithms. As the result of the study Neural networks gave the highest accuracy results of 0.85.