A 2021 Forbes article (Morgan, 2021) about “What Will Restaurants Look Like After Covid?” highlighted that restaurants will increasingly use technology such as digital menus to facilitate food ordering and maintain social distancing in order to reduce human interaction. Such drastic changes force restaurant owners to adopt technology that might not be optimised for a restaurant.
One such technology that the team has identified as an opportunity to improve upon is the digital menu. Using the lessons taught through the course, the team will be using tools such as Python, Neo4j, as well as techniques like TF-IDF and Expert Systems to build a menu item recommender based on web technology to help restaurateurs increase revenue.
Through this project, the team discovers a new understanding of creating a restaurant menu as well as the impact that recommendation brings. Not only that, the team also learned that the software developed here can also be easily applied to other businesses that provide a menu (such as retail) to drive up their business income.
| Official Full Name | Student ID (MTech Applicable) | Work Items (Who Did What) | Email (Optional) |
|---|---|---|---|
| Lee Joon Hui Jeremy | A0048174A | 1. Project research (linking dishes with ingredients) 2. Graph construction 3. Similar items recommendation model (Server side) 4. Backend design, setup and deployment 5. UI/UX design and prototyping 6. Project report 7. Video recording for Business case explanation |
jeremyleejh@u.nus.edu |
| MU AOHUA | A0121924M | 1. On-device similar items recommendation model. 2. Complentary item recommendation. (On-device and Server) 3. Menu offline support 4. User interface development 5. Deploy frontend to firebase 6. Project report 7. Video recording for Tech explanation |
e0689785@u.nus.edu |
Node: v14.6.0 and above Yarn: 1.22.4 and above Browser: Latest Chrome or Firefox OS: MacOS DB setup MongoDB installation Clean installation without username and password
Neo4J installation Create a new account with default username neo4j and with the password asdf
Seeding Neo4J Database Run the 2 cypher scripts here in sequence
https://github.com/aohua/KG-food/blob/main/db/kg_food_db.cypher
https://github.com/aohua/KG-food/blob/main/db/kg_food_complementary.cypher
Start both MongoDB and Neo4J databases before running the actual backend Access to the backend server code and execute the shell command, run.sh
sh run.sh
All the frontend code is under food-app-web-pwa folder.
You can access the deployed version here: https://kg-food.web.app/
To run the project locally:
Install Node and Yarn: brew install node brew install yarn
Install dependencies: cd food-app-web-pwa yarn install
Start the dev server: yarn start
You can now access the web app at: http://localhost:3000/
Before you start to use the app, please make sure that you have already done the backend setup and have the backend server running at http://127.0.0.1:5000
Refer to report here: https://github.com/aohua/KG-food/blob/main/ProjectReport/Project%20Report%20Contactless%20Menu%20Recommendation.pdf
https://github.com/aohua/KG-food/blob/main/Miscellaneous/Putien%20Menu%20%20-%20Menu.pdf
GrabFood https://github.com/aohua/KG-food/blob/main/Miscellaneous/grabfood-first-load.png https://github.com/aohua/KG-food/blob/main/Miscellaneous/grabfood-subsequent-load.png
Digital Menu Provider https://github.com/aohua/KG-food/blob/main/Miscellaneous/imakan-first-load.png https://github.com/aohua/KG-food/blob/main/Miscellaneous/imakan-subsequent-load.png
PDF (NOTE: We are unable to capture 1st load due to constant timeout) https://github.com/aohua/KG-food/blob/main/Miscellaneous/pdf-load.png
Our Solution https://github.com/aohua/KG-food/blob/main/Miscellaneous/putien-first-load.png https://github.com/aohua/KG-food/blob/main/Miscellaneous/putien-subsequent-load.png
https://github.com/aohua/KG-food/blob/main/Miscellaneous/Recommendation-survey-result.png
