Recommender Systems Questions Medium
The item-based collaborative filtering algorithm is a technique used in recommender systems to provide personalized recommendations to users based on their preferences and similarities between items.
In this algorithm, the system first builds a similarity matrix that measures the similarity between each pair of items. This similarity can be calculated using various methods such as cosine similarity or Pearson correlation coefficient.
Once the similarity matrix is constructed, the algorithm identifies the items that are most similar to the ones the user has already interacted with or rated positively. It then recommends items that are highly rated by users who have similar preferences to the current user.
The item-based collaborative filtering algorithm is advantageous as it can handle large datasets efficiently and is less affected by the sparsity problem compared to user-based collaborative filtering. It also allows for easy updates and scalability as new items can be added to the system without requiring a complete re-computation of the similarity matrix.