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.

Artificial Intelligence & Machine Learning Application

Our company advertises thousands of products, and we want to leverage the latest technology to gain market advantage. Applications of this technology include recommendation algorithms, predictive analytics like lifetime values, fraud detections, and classifications.We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications.This will involve several different steps for the students, including:Conducting background research on our existing products and the dataset.Analyzing our current dataset.Researching the latest AI / ML techniques and how they could be applied to our data.Developing an AI / ML model that provides unique outcomes or insights into our data.Providing multiple solutions that can be applied to solve the same problem.

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.