The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses


Most recommender systems are evaluated on how they accu- rately predict user ratings. However, individuals use them for more than an anticipation of their preferences. The literature demonstrated that some recommendation algorithms achieve good prediction accuracy, but suffer from popularity bias. Other algorithms generate an item category bias due to unbalanced rating distributions across categories. These ef- fects have been widely analyzed in the context of books, movies, music, and tourism, but contrasting conclusions have…