Smart exploration methods for mitigating item cold-start problem in collaborative filtering recommendation systems

Abstract

Systems and methods for building a latent item vector and item bias for a new item in a collaborative filtering system are disclosed. The method includes dividing incoming users into intervals with each interval having a learning phase and a selection phase. The learning phase scores each incoming user according to a best estimate latent vector and bias and saves the highest score. In the selection each incoming user is scored and a user exceeding the highest score is selected. The best estimate latent vector and bias is then updated based on the user's vector and bias, and the user's interaction with the item. The updated best estimate latent vector is then used in further intervals for learning and selecting users.