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arXiv · 1406.2235

A Hybrid Latent Variable Neural Network Model for Item Recommendation

Michael R. Smith·Tony Martinez·Michael Gashler
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Abstract

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.

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Michael R. Smith, Tony Martinez, Michael Gashler. 2014-06-09. A Hybrid Latent Variable Neural Network Model for Item Recommendation. https://arxiv.org/abs/1406.2235

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