[Paper Review] Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback.

Qibing Li, Xiaolin Zheng|arXiv (Cornell University)|Dec 25, 2017

Recommender Systems and Techniques45 references8 citations

TL;DR

This paper proposes Deep Collaborative Autoencoder (DCAE), a unified deep learning framework for recommender systems that jointly models explicit and implicit feedback using non-linear representation learning. By integrating three-stage pre-training, popularity-based error reweighting, and sparsity-aware data augmentation, DCAE achieves state-of-the-art performance across three real-world datasets, significantly improving recommendation accuracy while mitigating overfitting in implicit settings.

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 works. First, most works perform deep content feature learning and resort to matrix factorization, which cannot effectively model the highly complex user-item interaction function. Second, due to the difficulty on training deep neural networks, existing models utilize a shallow architecture, and thus limit the expressiveness potential of deep learning. Third, neural network models are easy to overfit on the implicit setting, because negative interactions are not taken into account. To tackle these issues, we present a novel recommender framework called Deep Collaborative Autoencoder (DCAE) for both explicit feedback and implicit feedback, which can effectively capture the relationship between interactions via its non-linear expressiveness. To optimize the deep architecture of DCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, we propose a popularity-based error reweighting module and a sparsity-aware data-augmentation strategy for DCAE to prevent overfitting on the implicit setting. Extensive experiments on three real-world datasets demonstrate that DCAE can significantly advance the state-of-the-art.

Motivation & Objective

• Address the limitation of existing deep learning models in capturing complex user-item interaction functions due to reliance on matrix factorization.
• Overcome the expressiveness bottleneck in deep neural networks caused by shallow architectures in current recommendation models.
• Mitigate overfitting in implicit feedback settings where negative interactions are not explicitly modeled.
• Develop a unified framework that effectively handles both explicit and implicit feedback within a single deep learning architecture.

Proposed method

• Propose a Deep Collaborative Autoencoder (DCAE) architecture that learns non-linear user and item representations through stacked autoencoders.
• Implement a three-stage pre-training strategy combining supervised learning on explicit feedback, unsupervised feature learning, and fine-tuning for joint optimization.
• Introduce a popularity-based error reweighting module that down-weights loss for popular items to reduce overfitting in implicit feedback.
• Apply a sparsity-aware data-augmentation technique to artificially increase negative sample diversity and improve generalization.
• Use collaborative filtering signals from both user and item encoders to reconstruct user-item interactions in a joint embedding space.
• Optimize the end-to-end model using backpropagation with adaptive learning rates and regularization to enhance convergence and robustness.

Experimental results

Research questions

• RQ1 Can a unified deep learning framework effectively model both explicit and implicit feedback in recommendation systems?
• RQ2 How does three-stage pre-training improve the representation quality and convergence of deep autoencoders in collaborative filtering?
• RQ3 To what extent does popularity-based error reweighting reduce overfitting in implicit feedback scenarios?
• RQ4 How does sparsity-aware data augmentation enhance model generalization on sparse user-item interaction matrices?
• RQ5 Does the proposed DCAE framework outperform existing state-of-the-art models on real-world recommendation benchmarks?

Key findings

• DCAE achieves significant performance gains over existing state-of-the-art models on three real-world datasets, demonstrating superior recommendation accuracy.
• The three-stage pre-training mechanism enhances model convergence and representation quality by leveraging both explicit and unsupervised signals.
• Popularity-based error reweighting effectively reduces overfitting on popular items in implicit feedback settings, improving generalization.
• Sparsity-aware data augmentation increases the diversity of negative samples, leading to more robust and stable training dynamics.
• The unified DCAE framework successfully handles both explicit and implicit feedback without requiring separate model heads or architectures.
• Empirical results show consistent improvements across multiple evaluation metrics, confirming the effectiveness of the proposed components in isolation and in combination.