RankDash

Hotel Recommendation Engine

We built a hotel recommendation engine with an ensemble collaborative filtering approach. We pair this with beautiful, interactive data visualizations built on top of D3.js, running live in the browser to create a dynamic story with the data.

To build our hotel recommendation system, we first created a set of ratings, based on a weighted average of user actions associated with a particular hotel.

We applied the following models:

1. Matrix Factorization
2. Non-Negative Matrix Factorization
3. Hierarchal Multilayer Model

We created a fully responsive web dashboard to host our visualizations. These visualizations include:

1. Geographical Heat Maps of Quantitative Features
2. Feature Histograms
3. Topographical Higher-Dimensional representations of sparse matrix before training + dense matrix learned by model
4. Network Graph Summarizing Recommendation Results

Built With

• bash
• css3
• d3.js
• dash
• html5
• javascript
• jupyter-notebook
• python
• react