Hybrid content and collaborative filtering based recommendation system for e-learning platforms

Abstract

Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of e-learning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to generalize across content sources and content types. In this work, we present a hybrid recommendation system that combines the desirable characteristics of collaborative filtering, as well as from content-based filtering, for the task of recommending course content/curriculum to users of an e-learning system. Our recommendation easily incorporates changing user profiles (as learners step through course content) and also generalize across content sources (courses taught by various departments) and types. We apply our system on a real dataset comprising 111 students organized into interdisciplinary groups. Our results showcase the clear benefits that our hybrid recommendation system enjoys, showing more than 30 percentage points of improvement over conventional filtering techniques.