This project develops an AI-driven system to enhance hospitality guest experiences using Large Language Models (LLMs) like OpenAI GPT and Meta LLaMA. The system analyzes guest feedback, monitors sentiment trends, and dynamically personalizes recommendations for dining, activities, and amenities. By integrating real-time alerts for service teams, the solution ensures adaptive, tailored experiences that evolve with guest preferences during their stay.
- Personalized recommendations for dining, activities, and amenities based on guest behavior analysis.
- Real-time sentiment monitoring to proactively address guest feedback.
- Increased guest satisfaction through dynamic personalization.
- Automated alerts for staff to resolve issues and optimize service delivery.
The project is structured into four milestones, each building upon the previous one to develop a robust AI-powered guest experience system.
Objective: Predict the favorite dish of a customer based on their dining preferences and behavior.
Key Steps:
- Data Preparation – Loaded the dataset, split it into feature extraction, training, and testing sets based on time-based logic.
- Feature Engineering – Computed customer-level and cuisine-level features.
- Data Integration – Merged engineered features into the training and testing datasets.
- Encoding & Model Training – Applied one-hot encoding for categorical data and trained an XGBoost Classifier for dish prediction.
- Model Evaluation – Assessed model performance using accuracy, log loss, and feature importance analysis.
✅ Outcome: Developed an XGBoost model for predicting a guest’s favorite dish with 0.18 accuracy.
Objective: Develop a recommendation engine to suggest personalized amenities and activities for guests.
Key Steps:
- Extracted features like frequency of activity participation, preferred times, and past spending patterns.
- Implemented User-Based & Item-Based Filtering to recommend relevant activities based on guest preferences.
- Used TF-IDF & Cosine Similarity to generate activity recommendations based on textual guest preferences.
- Combined collaborative and content-based approaches for improved accuracy.
✅ Output: Built a Hotel Booking UI with Personalized Recommendations that adapts to guest preferences and historical behaviors.
Objective: Analyze guest reviews using Natural Language Processing (NLP) to monitor sentiment trends and generate real-time alerts.
Key Steps:
- Loaded and cleaned guest feedback data, tokenized text, and removed stopwords and Created Embeddings and Inserting Embeddings and Metadata into index
- Used Sentiment Analysis for rule-based sentiment scoring for deeper insights.
- Tracked sentiment over time, identified top positive/negative topics.
- Created automated alerts for Manager when negative sentiment was detected for hospitality.
✅ Outcome: Implemented real-time guest sentiment tracking, allowing proactive issue resolution and improved guest satisfaction.
Key Steps:
- Created interactive dashboards for customer insights, sentiment analysis, and recommendations.
- Used Together.AI for graph-based visualization of guest behavior patterns.
- Provided real-time tracking of service efficiency and guest satisfaction trends.
- Integrated all milestones into a seamless, user-friendly visualization interface.
✅ Output: created Dashboards for the Hotel Bookings, Hotel Dining Insights, Customer Review Analysis
- Integrated all the User Interfaces using auth.py as a login/signup page making it user friendly for all the customer requirements.
- Improve Model Accuracy: Experiment with hyperparameter tuning and deep learning models.
- Expand Sentiment Monitoring: Integrate audio sentiment analysis from voice feedback.
- Enhance Chatbot Capabilities: Include multi-language support and voice-based interactions.
- Deploy as a SaaS Solution: Develop an API for seamless hospitality industry integration.
🚀 AI-Driven Guest Experience Personalization System for Hospitality – Redefining Hospitality with AI!