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.