  Collaborative filtering is used to recommend items to a user without
requiring a knowledge of the item itself and tends to outperform other
techniques. However, collaborative filtering suffers from the cold-start
problem, which occurs when an item has not yet been rated or a user has not
rated any items. Incorporating additional information, such as item or user
descriptions, into collaborative filtering can address the cold-start problem.
In this paper, we present a neural network model with latent input variables
(latent neural network or LNN) as a hybrid collaborative filtering technique
that addresses the cold-start problem. LNN outperforms a broad selection of
content-based filters (which make recommendations based on item descriptions)
and other hybrid approaches while maintaining the accuracy of state-of-the-art
collaborative filtering techniques.
