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Productization Challenges of Contextual Multi-Armed Bandits
Contextual Multi-Armed Bandits is a well-known and accepted online optimization algorithm, that is used in many Web experiences to tailor content or presentation to users' traffic.
Search-Based Serving Architecture of Embeddings-Based Recommendations
Over the past 10 years, many recommendation techniques have been based on embedding users and items in latent vector spaces, where the inner product of a (user, item) pair of vectors represents the predicted affinity of the user to the item.
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history.
Distributed Exploration in Multi-Armed Bandits
That is, distributing learning to $k$ players gives rise to a factor $\sqrt{k}$ parallel speed-up.
OFF-Set: One-pass Factorization of Feature Sets for Online Recommendation in Persistent Cold Start Settings
Assuming certain features or attributes of users are known, one approach for handling new users is to initially model them based on their features.
