Automated Recommendation Systems for Airbnb Users
Develop an automated price recommendation system for Airbnb hosts. By considering nearby listings based on geolocation, hosts can receive guidance on setting optimal nightly prices. The system calculates the recommended price as the average of nearby properties' nightly rates. Customization options include the number of neighboring properties to consider and the desired room type.
This project focuses on developing an automated price recommendation system for hosts on the Airbnb platform. Hosts often struggle with determining the optimal nightly price for their property, and this recommender system aims to provide them with guidance based on existing listings.
The price recommender system requires the user to provide the geolocation (latitude and longitude) of their property. The algorithm then searches for nearby properties based on Euclidean distance in terms of latitude and longitude. The recommended price is calculated as the average of the nightly prices of these closeby properties. By considering similar properties in the vicinity, hosts can make more informed decisions about pricing their listings.
To enhance customization, the recommender system allows users to set the number of neighbouring properties to be considered. A larger number of neighbours indicates a broader geographical area for price comparison. Additionally, hosts can specify the desired room type, ensuring that only properties of the selected room type are taken into account for price recommendations.
Programming Language: Python