Recommendation Engine Development

Our company advertises thousands of products through a user interface. We would like to work with students to create a new recommendation system that returns products based on what a user has previously viewed or rated. The recommendations should also consider products viewed by other users who are like the given user. This will involve several different steps for the students, including: Analyzing our existing dataset of users, products, and reviews. Developing a recommendation engine software. Optimizing software runtime performance and assessing areas for improvement. Researching other variables that can improve the quality of product recommendations. Accounting for additional variables in the recommendation engine software. Testing the developed software and making improvements based on additional data.

Recommendation Engine Prototype

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.