Neural Collaborative Autoencoder

TL;DR

The paper introduces Neural Collaborative Autoencoder (NCAE), a versatile deep learning framework for collaborative filtering that effectively handles both explicit and implicit feedback, overcoming training and overfitting challenges.

Contribution

It proposes a novel neural autoencoder architecture with a three-stage pre-training and strategies to prevent overfitting, advancing recommendation system performance.

Findings

NCAE outperforms existing models on three real-world datasets.

The three-stage pre-training improves deep model optimization.

Error reweighting and data augmentation reduce overfitting in implicit feedback scenarios.

Abstract

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing models. First, these models cannot work on both explicit and implicit feedback, since the network structures are specially designed for one particular case. Second, due to the difficulty on training deep neural networks, existing explicit models do not fully exploit the expressive potential of deep learning. Third, neural network models are easier to overfit on the implicit setting than shallow models. To tackle these issues, we present a generic recommender framework called Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback. NCAE can effectively capture the subtle hidden relationships between interactions via a non-linear matrix factorization process. To optimize the deep architecture of NCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, to prevent overfitting on the implicit setting, we propose an error reweighting module and a sparsity-aware data-augmentation strategy. Extensive experiments on three real-world datasets demonstrate that NCAE can significantly advance the state-of-the-art.