A Hybrid Latent Variable Neural Network Model for Item Recommendation
This work addresses the cold-start issue in item recommendation systems, which is an incremental improvement over existing hybrid approaches.
The paper tackles the cold-start problem in collaborative filtering by introducing a hybrid neural network model with latent input variables (LNN), which outperforms content-based filters and other hybrid methods while matching state-of-the-art collaborative filtering accuracy.
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.