Online Learning of Structured Matrices in Recommendation Systems
Mina Karzand (TTIC)
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing ‘like’ or ‘dislike’ feedback. A latent variable model specifies the user preferences: both users and items are clustered into types. The model captures structure in both the item and user spaces, and our focus is on the simultaneous use of both structures. We analyze the situation in which the type preference matrix has i.i.d. entries. Our analysis elucidates the system operating regimes in which existing algorithms are nearly optimal, as well as highlighting the sub-optimality of using only one of item or user structure (as is done in commonly used item-item and user-user collaborative filtering). This prompts a new algorithm that is optimal in essentially all parameter regimes.