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Airbnb recommender system using clustering and interactive visualizations

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Airbnb Recommender System

Final Project for CSCI 6612: Visual Analytics

Dalhousie University, Fall 2019

Introduction

This project aims to construct a recommender system for Airbnb listings. As the coursename dictates, this project consists of two significant parts, machine learning, and visualization.On the machine learning side, core implementation is a K-means clustering algorithm to clusterlistings. On the other part, some of the key features are maps, listing details, and some analysisto ease the choice among the options. Although establishing a machine learning system topredict some of the features in the context of Airbnb is not the goal of this project, we havereached some good results in terms of accuracy. Using the recommender system, the searchspace for the users will be narrowed down to a select set of options to choose from among them.

Live Demo on Heroku

Running the project:

cd flask
virtualenv -p python3 .\venv
.\venv\bin\activate.bat
pip install -r requirements.txt
python main.py

After you have the backend running, please navigate to: http://localhost:5000/

Dataset

Melbourne Airbnb Open Data

Map view

Following is the list of available base layers and overlays to choose from using the toolbox on the top right of the map.

  • Street view
  • Price heatmap view
  • Airbnb listings
  • Tourist attractions
  • Neighborhoods

Fitering listings

Use "Open Sidebar" and use the input form and "filter" button.

Filtering area on map

Use the toolbox on the right of map, the circle icon for drawing a circle, edit and trash buttons for editing and removing the circle. Once you defined the circle, use "Filter Area(Select on map)" button for filterin.

  • If you deleted a circle, please use "Filter Area(Select on map)" button again to see all airbnbs in the map again.

Airbnb review sentiment analysis

Select an Airbnb from the listings and click on the "Analyze reviews" button on the pop-up.

Clustering

Select number of clusters with "Number of K-Means clusters" input and use sliders to adjust weight of each feature for clustering. Then use "cluster" button to see the results on map. After each adjustment of sliders you need to push "cluster" button again to see the results.

clustering

cluster analysis

adjusting weight of attributes

Contributors

Soheil Latifi

Asal Jalilvand

Kewei Ma

Mirerfan Gheibi

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Airbnb recommender system using clustering and interactive visualizations

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