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New York Airbnb Accommodation Recommender

This project provides a recommendation system for New York Airbnb accommodations using combined features such as text descriptions, images, price, and location data.

The dataset used for this project was collected from Inside Airbnb.

📥 Dataset

The full prepared dataset can be accessed using this link.

🚀 How to Use

Before running the application, ensure the KNN model and combined features are saved. You can generate these by running the notebook:

save_compute_similarities.ipynb

To launch the Streamlit app, execute the following command:

streamlit run airbnb_knn.py

🛠️ Implementation Pipeline

The project implementation follows the pipeline below:

Implementation

🎯 Project Goals

  • Build a robust recommender system combining multiple features (price, description, images, location).
  • Utilize advanced text analysis (TF-IDF, Sentiment Analysis).
  • Compute similarity between listings using Cosine Similarity and KNN.

Key Methods

  1. TF-IDF (Term-Frequency, Inverse Document Frequency Vectorizer)
    • Quantifies the importance of a term in a document relative to its frequency and rarity across multiple documents.
      • Term Frequency (TF): Relative frequency of a term in a document.
      • Inverse Document Frequency (IDF): Measures how rare a term is across all documents.
  2. Sentiment Analysis
    • Sentiment analysis is performed using VADER (Valence Aware Dictionary and sEntiment Reasoner).
    • Reference: Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.
    • VADER generates a Compound Score: a normalized sum of lexicon ratings ranging from -1 (most negative) to +1 (most positive).
  3. Image Features
    • Image Histograms (HSV): Histograms of Hue, Saturation, and Value capture the pixel value distribution in images.
    • Similarity: Image similarity is computed by comparing HSV histograms.
  4. Cosine Similarity
    • Cosine similarity is calculated between textual descriptions and image HSV vectors to measure feature similarity.
  5. K-Nearest Neighbors (KNN)
    • Recommendations are generated using the KNN algorithm with:
      • k = 10
      • Combined features: price, location, image similarity, description similarity, and polarity.