Glossary term
Glossary term
Memory and Retrieval
In recommendation systems, an embedding vector generated by matrix factorization that holds latent signals about user preferences. Each row of the user matrix holds information about the relative strength of various latent signals for a single user. For example, consider a movie recommendation system. In this system, the latent signals in the user matrix might represent each user's interest in particular genres, or might be harder-to-interpret signals that involve complex interactions across multiple factors.
The user matrix has a column for each latent feature and a row for each user. That is, the user matrix has the same number of rows as the target matrix that is being factorized. For example, given a movie recommendation system for 1,000,000 users, the user matrix will have 1,000,000 rows.
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Created for this library
A streaming recommender team factorizes its rating matrix into a user matrix and an item matrix whose rows represent user latent factors.
An e-commerce recommender team stores the user matrix of latent factors for fast retrieval and ranking at request time.
A music platform stores the user matrix so similar listeners can be identified quickly for collaborative filtering.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License