Hotel Recommendation using Feature and Machine Learning Approaches: A Review

In recent years, the entertainment and tourism industries have increased their use of recommendation systems. They may filter heterogeneous data from several networks to provide clients useful suggestions. Tourists and vacationers depend on internet reviews, star ratings, and facilities to find the finest hotel in every location. Online hotel reservation systems grade hotels based on customer ideas to attract more customers. A hotel recommendation system tries to estimate which hotel a user would choose. It’s crucial to help people choose the best hotel within their budget. Customer feedback would be helpful here. This paper explores SVM, Random Forest, and other approaches. This essay will illustrate that it is possible to enhance the field by offering an overview of existing research and outlining the hurdles for crowdsourcing online recommendations of tourist resources. This initial study aimed to help travelers identify suitable lodgings.