Collaborative and Content-Based Filtering
Explore how to build and apply collaborative and content-based filtering methods for recommender systems. Understand turning interaction data into user-item matrices, using cosine similarity for recommendations, and leveraging course content with TF-IDF. Learn to choose the best approach depending on data availability and challenges like cold starts and sparsity to improve personalized course suggestions.
Given a learner’s past course completions, which continuing-education courses should appear next? Recommending the same most-popular course to every employee ignores what each employee has already studied and can leave important compliance gaps unaddressed.
An interactions table records behavior with columns such as user_id, item_id, and interacted. Here, interacted=1 is implicit feedback, meaning observed behavior such as viewing, saving, or completing a course rather than a stated rating. A user-item matrix rearranges those records so each row is a user, each column is an item, and each cell shows an interaction. A top-N recommendation is a ranked list of the
We will build two recommenders for small security and compliance course catalogs. Each code example defines its own small set of courses, so course IDs and catalog size differ between examples. Collaborative filtering uses patterns in employee behavior. Content-based filtering uses course text such as topics and descriptions.