Hotel Recommendation Systems With Intelligent Data Analytics Collaborative Filtering Based On Machine Learning

Abstract

Both academics and practitioners have emphasized the significance of excellent customer relations and subsequent word-of-mouth in various industrial sectors. Eco-friendly (green) products and services have gotten a lot of attention as a result of customers' developing concern about environmental sustainability and sensitivity to the deterioration of the environment. Yatra is a popular e-tourism website. For this platform's recommendation engine, understanding and anticipating visitor preferences utilizing modern development of data analytics technologies is a major undertaking. They present a new soft computing strategy known as machine task in this paper learning approach for finding the best matched eco-friendly hotels in Yatra depending on a variety of quality parameters. To increase the sustainability of forecasts from a huge number of user scores, they designed a system that combines pattern recognition and predicting computational strategies. On a huge dataset obtained from the Yatra platform, the suggested soft computing approach is assessed. The findings demonstrate that processing a large number of data reduction and forecast network learning algorithms are useful reviews on eco-friendly hotel characteristics and predicting visitors' eco-friendly hotel choices in Yatra.