Hotel clustering and recommendations for specific users

Recently, individuals have gained massive data by the means of internet and managed them. So data filtering becomes as useful as data retrieval. Many methods have been developed that can be used for this task namely Content based filtering, Collaborative filtering and of lately, some people have started using machine learning for this process as well. Recommender systems have been developed for this filtering tasks. The principle task of recommender systems to predict client's inclination from the rated data, channels a few things from huge data, and proposes applicant things for client. By and large, recommender system has been utilized for help of choosing things. Recommender systems have been used extensively in e-commerce and more often in hotel recommendation as well. In this project, we have tried to implement a hybrid recommender system for Airbnb listing from 6 different cities and 100 listings each. A hybrid approach means that we have tried to implement recommender system with three different methods which are content based, collaborative and machine learning as well. We have considered many different features such as user ratings, user reviews, place summary and many other host features that would help the customers select the property they want to live in and recommend them using past experiences and ratings. We have also tried to include the purpose of the trip of users and time duration for better recommendation.