Adobe Inc.
Personalized recommendations using localized regularization

Abstract:

A subset of items that can be identified, promoted, or recommended to the user is determined based in part on rankings or other feedback that the user has given to other items in the set. The techniques discussed herein employ localized regularization to generate estimated values for the unknown values. Regularization refers to adding information into the system in order to generate the unknown values. This additional information of the system is an estimate, and is generated based on the known properties of the system. The techniques discussed herein employ localized regularization, which refers to estimating additional information based on the particular user for which the unknown values are being generated. In contrast to employing global regularization that treats all users in the system the same, the localized regularization discussed herein treats each user independently of the other users.