Recommender System 편집하기 (부분)

==How Recommender Systems Work==
Recommender systems use a variety of techniques and data sources to make predictions. These include:
*'''User Data''': Information on user activity, preferences, purchase history, and ratings.
*'''Item Data''': Characteristics or features of items, such as categories, tags, or genres.
*'''Similarity Metrics''': Algorithms that calculate similarity between users or items, such as cosine similarity, Jaccard index, or Pearson correlation.
*'''Machine Learning Models''': Advanced models like matrix factorization, deep learning, or clustering methods to learn patterns in user and item data.