Contextual bandit algorithms are commonly used in recommender systems, where content
popularity can change rapidly. These algorithms continuously learn latent mappings
between users and items, based on contexts associated with them both. Recent
recommendation algorithms that learn clustering or social structures between users have
exhibited higher recommendation accuracy. However, as the number of users and items in
the environment increases, the time required to generate recommendations deteriorates …

Contextual bandit algorithms are commonly used in recommender systems, where content
popularity can change rapidly. These algorithms continuously learn good mappings
between users and items, based on contexts associated with both the users and items.
Recent recommendation algorithms that learn clustering or social structures between users
have exhibited higher recommendation accuracy. However, as the number of users and
items in the environment increases, the time required to generate recommendations …