Our company has a website for our customers to interact with products. The website welcomes thousands of users every day, each with different experiences and preferences. We would like a group of students to design and build a prototype recommendation engine that matches users to products. These recommendations could be based on what other users with similar viewing/liking patterns have viewed/liked.

This will involve several different steps for the students, including:

• Familiarizing yourself with our website and products to understand how they work.
• Researching state-of-the-art machine learning and recommendation engine technologies.
• Developing recommendation engine prototype models based on an existing dataset.
• Producing recommendations from the prototype models.
• Testing recommendation engine prototype models with users.
• Iterating and improving tested prototype models.

By the end of the project, students should demonstrate:

• Understanding of our website and products
• Understanding of machine learning and recommendation engine technologies
• Production of recommendations using prototype models

Bonus steps would include:

• Testing prototype models with users and iterating designs to improve them

Final deliverables should include

• Code developed for the recommendation model.
• Recommendations supporting the model’s validity, which may be assessed subjectively.
• A written report describing the technologies that were used, the challenges that were faced, the model’s final outcomes, and recommendations for the project’s next steps.

Students will connect directly with us for mentorship throughout the project. We will be able to provide answers to questions such as:

• Our website and products
• Common concepts related to machine learning and recommendation engines
• Prototyping and its role in our design process
• Input on choices, problems or anything else the students might encounter.