https://github.com/jamesrobionyrogers/content-recommender-assessment
Content Recommender is the project I have been working on for the Project Management NZCEA Level 3 standard. I started the project on 26th August and finished on the 10th October 2021
https://github.com/jamesrobionyrogers/content-recommender-assessment
assessment-project ecs100 java
Last synced: 2 months ago
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Content Recommender is the project I have been working on for the Project Management NZCEA Level 3 standard. I started the project on 26th August and finished on the 10th October 2021
- Host: GitHub
- URL: https://github.com/jamesrobionyrogers/content-recommender-assessment
- Owner: JamesRobionyRogers
- Created: 2021-08-26T09:18:53.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-05-31T12:58:29.000Z (about 3 years ago)
- Last Synced: 2025-04-12T06:48:31.319Z (2 months ago)
- Topics: assessment-project, ecs100, java
- Language: Java
- Homepage:
- Size: 17.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url]
Content Recommender Java
26th of August - 10th October 2021
Here is the ReadMe file for my Content Recommender Java Program. For this assignment we were tasked to create a recommender system given a dataset of movies or music albums, users and their ratings
___
About The Project
AS 91901: Apply user experience methodologies to develop a design for a digital technologies outcome
Content Recommender is the project I have been working on for the Project Management NZCEA Level 3 standard. I started the project on 26th August and finished on the 10th October
#### Problem Statement
Recommender systems are commonly recognised as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter.
“In October 2006, Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch” (Bennett & Lanning, 2007).
Given a dataset of movies or music albums, users and their ratings, you are to create a recommender system.
### Built With
* Java JDK 15 (Maven Project)
* ECS100 Library
* UiBooster Library
## Getting Started
Getting started using this project is quite easy. Download a local copy of the project, navigate to the project directory ```recommener/srs/main/java/com/robionyrogers/GUI.java``` and run the file.
This project has been developed using Java JDK 15 with the most recent test using JDK 17.0.1
### Installation
1. Clone the repo using Git
```sh
git clone https://github.com/JamesRobionyRogers/Content-Recommender-Assessment.git
```
2. Download the zip file from above
3. Run the file ```GUI.java``` found under: ```recommener/srs/main/java/com/robionyrogers```
## Usage
Here are some screenshots of the program in use. Below we have a series of speraerate screens found in the outcome
##### Menu and View All Content screens
##### UiBooster Window Inputs
## Project Summary
The problem I was given for this project was to create a recommender system with a GUI using Java and the ECS100 library and a given dataset of movies or music albums, users and their ratings.
For this project I followed a project management approach which included conculting with potential clients / stakeholders, using the project mangement tools [ClickUp](https://www.clickup.com) and Github. As a result of this I developed the program through breaking the problem down into manageable componenents. More of this process can be seen in my documentation `13DTC Project Management.docx`
Developing it I used Java, the ECS100 library as well as the UiBooster library which I learnt how to use from reading the documentation.
Overall this project was a success. A couple of things I would like to improve on moving forward would be to save the users added movies/tv shows in a file so so you can open up where you left off. Another major improvement could come by incorperating the MovieDB's api in order to improve recommendations.
##### Tasks Moving Forward
- [ ] Read the FIXME/TODO comments in the code
- [ ] Use the MovieDB api for better recommendations
[contributors-shield]: https://img.shields.io/github/contributors/jamisbuggerlugs/Python_Tutorial_Website.svg?style=flat-square
[contributors-url]: https://github.com/JamisBuggerlugs/Python_Tutorial_Website/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/JamisBuggerlugs/Python_Tutorial_Website.svg?style=flat-square
[forks-url]: https://github.com/JamisBuggerlugs/Python_Tutorial_Website/network/members
[stars-shield]: https://img.shields.io/github/stars/JamisBuggerlugs/Python_Tutorial_Website.svg?style=flat-square
[stars-url]: https://github.com/JamisBuggerlugs/Python_Tutorial_Website/stargazers
[issues-shield]: https://img.shields.io/github/issues/JamisBuggerlugs/Python_Tutorial_Website.svg?style=flat-square
[issues-url]: https://github.com/JamisBuggerlugs/Python_Tutorial_Website/issues
[license-shield]: https://img.shields.io/github/license/JamisBuggerlugs/Python_Tutorial_Website.svg?style=flat-square
[license-url]: https://github.com/JamisBuggerlugs/Python_Tutorial_Website/blob/master/LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
[linkedin-url]: https://linkedin.com/in/JamisBuggerlugs
[product-screenshot]: imgs/readme-assets/desktop-home-light.png