Our company has a web-based service for our customers who are from North American Institutes like

1. Universities with user-product interactions are a recommendation system.

2. colleges at one end of the interface providing services

Postsecondary institutes are the other clients from Global locations who use our software to connect with

institutes for admission

We would like a group of students to design and build a prototype recommendation engine for our service that matches users to products. These recommendations could encompass:

• matching programs with applicants based on preferences in skills and background and financial parameters

This project will involve:

1. Students become familiar with our product and understand generally how it works
2. Students should spend time conducting research on state-of-the-art machine learning, and recommendation engine technologies
3. Students are free to build their prototype model as they see fit

Students should produce three different deliverables by the end of this project.

First, the actual source code of their recommendation model. If students choose for example to build a neural network based on Python, they should be prepared to submit the source code via a source control repository, such as git, to us.

Second, students should produce a set of actual recommendations to prove the validity of their model. This set of recommendations should be large enough to show the outcomes of the recommendations, with potential subjective scores provided by the student based on the recommendations (provide a 1-5 rating on ‘how good was this recommendation for a potential user’).

Lastly, students should provide a written report that details:

• What technologies they used
• Final outcomes based on their model
• Any next steps they would recommend if they were to continue this project
• if the students were successfully able to develop the required software a one-time honorarium will be awarded to them

A walk-through of the product, as well as lighter technical details of it will be provided to students before they begin.

Common machine learning knowledge such as exploration into recommendation engines and common techniques will be provided to students as a starting point.

Students will be able to ask questions at any point during the process. The students will be supported with information throughout the process