Introduction:

What is a Hotel Recommendation System?

A hotel recommendation system aims at suggesting properties/hotels to a user such that they would prefer the recommended property over others.

Why is a Hotel Recommendation System required?

In today’s data-driven world, it would be nearly impossible to follow the traditional heuristic approach to recommend millions of users an item that they would actually like and prefer.

Hence, a Recommendation System solves our problem where it incorporates user’s input, historical interaction, and sometimes even users demographics to build an intelligent model to provide recommendations.

Objective of this Blog:

In this blog, we will cover all the steps that are required to build a Hotel Recommendation System for the problem statement mentioned below. We will do an end-to-end implementation from data understanding, data pre-processing, and the algorithms used along with their PySpark codes.

Problem Statement:_ Build a recommendation system providing hotel recommendations to users for a particular location they have searched for on xyz.com_

What type of data are we looking for?

Building a recommendation system requires two sources of data, explicit and implicit signals.

Explicit data is the user’s direct input, like filters (4 star rated hotel or preference of pool in a hotel) that a user applies while searching for a hotel. Information such as age, gender, and demographics also comes under explicit signals.

Implicit data can be obtained by users’ past interactions, for example, the average star rating preferred by the user, the number of times a particular hotel type (romantic property) is booked by the user, etc.

#algorithms #pyspark #machine-learning #recommendation-system #cosine-similarity