Mastering Machine Learning with R - Second Edition

User-based collaborative filtering

In UBCF, the algorithm finds missing ratings for a user by first finding a neighborhood of similar users and then aggregating the ratings of these users to form a prediction (Hahsler, 2011). The neighborhood is determined by selecting either the KNN that is the most similar to the user we are making predictions for or by some similarity measure with a minimum threshold. The two similarity measures available in recommenderlab are pearson correlation coefficient and cosine similarity. I will skip the formulas for these measures as they are readily available in the package documentation.

Once the neighborhood method is decided on, the algorithm identifies the neighbors by calculating the similarity measure ...