Providing User Specific LMS Content Recommendations Using Machine Learning

Most content consumption platforms face a similar dilemma of being able to target relevant content to users whenever it’s most appropriate. Content Recommendation Engines have popularized over the last decade, and are now mainstream components in our lives, powering applications like Netflix and Spotify. While some are meant to keep you engaged, others are targeted at saving time and sifting through noise to provide what’s most relevant. Eagle Point Software’s Pinnacle Series is one such content consumption platform, a Learning Management Solution (LMS) in the Architecture, Engineering, and Construction (AEC) space. Currently without a content recommendation system, users spend precious time manually browsing for content, time that detracts from designing and building critical infrastructure and world-impacting projects. In this report, I explore why addressing this problem is crucial for our industry, and cover the buildout of a hybrid content recommendation engine for Pinnacle Series using K-Nearest Neighbor and Neural Network models