The most common usage is User-Item-Item: user has a list of favorite items. Use the favorite items to find similar items (a.k.a Item-to-Item), and recommend these items to the user.
How to measure similarity: if user likes two items, and the two items have similarity. (Similar to Item Collaborative Filtering)
Another way to measure similarity: compare items' feature vectors.
User-User-Item: User 1 is similar to User 2. User 2 likes item A. So recommend item A to User 1.
User-Author-Item: User 1 likes Author 2, and Author 2 has item A. So, recommend item A to user 1.
User-Author-Athor-Item: User 1 likes Author2.Author 2 and Author 3 are similar. So recommend Author 3's items to User 1.
Using multiple models with separate shares in retrieving records, achieves better results than using the just one model to retrieve all items.
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