Hotel Recommendation System Based on Hybrid Recommendation Model

Hotel Recommendation System Based on Hybrid Recommendation Model

Abstract:

The Hybrid Recommendation Model-based Hotel Recommendation System uses Machine Learning and Sentiment Word Net to mine hotel reviews and summarize opinions using sentence relevance scores. This system helps web users quickly understand hotel reviews. Classifying and summarizing review data lets users quickly determine sentiment.

Introduction:

This project aims to create a Hotel Recommendation System that analyzes hotel reviews and extracts useful data. The system uses opinion mining to identify customer sentiments and rate them. The system ranks user reviews by sentiment using sentiment-based keywords in a database.

Objectives:

The goal of this project is to create a hotel recommendation system that uses a variety of techniques to analyze and extract valuable information from hotel reviews.

Project Details:

Hotel reviews are summarized in the Hotel Recommendation System web app. These reviews determine whether hotels are good, bad, or worst. The user’s search history and hotel page visits then generate recommendations.

The system uses a sentiment-based keyword database with positive or negative weights. This database ranks user review sentiment keywords. Users can review hotels after logging in. The system matches these reviews with keywords in the database and rates the hotel based on their rank.

Administration involves adding hotels and keywords to the database. This interactive app helps travelers and explorers. This system lets users choose a hotel before arriving.

Conclusion:

The Hybrid Recommendation Model-based Hotel Recommendation System uses Machine Learning and Sentiment Word Net to summarize and mine hotel reviews. The system analyzes hotel sentiment using opinion mining and sentiment keyword ranking. This system helps users find the best hotel for their needs. This app helps users choose hotels whether they travel often or rarely.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.