My solution for Web intelligence (2DV515) assignment 1 at Linnaeus University.
The assignment description can be found here: Assigmment 1
- Build a recommendation system that can find similar users and find recommendations for
a user, using the movies large dataset (see
/datasets/large) - You can verify that your application works by using the example dataset from the lecture (see
/datasets/example) - Use Euclidean distance as similarity measure
- Implement the system using a REST web service where:
- client sends a request to a server
- the server responds with json data
- the json data is decoded and presented in a client GUI
- Implement the Pearson Correlation similarity measure
- It shall be possible to select which measure to use from the client GUI
- Implement functionality for pre-generating an Item-Based Collaborative Filtering table by transforming the original data set
- Use the pre-generated table to implement a second way of finding recommendations for a user
- You shall only use Euclidean distance as similarity measure
- It shall be possible to select how to find recommendations from the client GUI (Item-Based or User-Based)
In the project directory, you can run:
The project will use the example data set.
This is short hand for npm run install -- ./datasets/example
If you´re using the large data set you can run this script to change data set.
The project will use the large data set.
If you´re using the example data set you can run this script to change data set.
Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.
You will also see any lint errors in the console.
The REST API will be available at http://localhost:3001
Both the page and the server will reload separately if you make edits.
You can either press ctrl + c or run npm stop to stop the docker containers.
Check if the docker containers are running or not.