CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (1804.06769v3)

Abstract: The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. In contrast to the matrix factorization based cross-domain techniques, our method is deep transfer learning, which can learn complex user-item interaction relationships. We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. CoNet is achieved in multi-layer feedforward networks by adding dual connections and joint loss functions, which can be trained efficiently by back-propagation. The proposed model is thoroughly evaluated on two large real-world datasets. It outperforms baselines by relative improvements of 7.84\% in NDCG. We demonstrate the necessity of adaptively selecting representations to transfer. Our model can reduce tens of thousands training examples comparing with non-transfer methods and still has the competitive performance with them.

An Overview of CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

The paper "CoNet: Collaborative Cross Networks for Cross-Domain Recommendation" by Guangneng Hu, Yu Zhang, and Qiang Yang focuses on addressing the data sparsity issue common in recommender systems by employing a novel transfer learning approach for cross-domain recommendation. This approach applies neural networks to facilitate deep transfer learning, contrasting with traditional matrix factorization (MF) techniques.

Summary of the Methodology

The proposed model, CoNet, leverages collaborative cross networks which operate by enabling dual knowledge transfer across domains. It introduces cross connections between the hidden layers of two base networks—each corresponding to a different domain. These connections facilitate knowledge transfer between domains via cross-domain mappings in a multi-layer feedforward network. The network also incorporates joint loss functions and is trained efficiently through back-propagation.

A distinctive feature of CoNet is its ability to adaptively select which representations to transfer between domains. This is achieved by integrating a sparse model variant that constrains the cross-domain mappings using sparsity-inducing regularization techniques such as lasso regularization. This adaptive selection aims to transfer only the most relevant representations, thereby improving the generalization and effectiveness of the cross-domain recommendation.

Empirical Evaluation

The CoNet model is empirically validated on two large-scale datasets, demonstrating significant improvements in recommendation performance over various baselines. Most notably, CoNet achieves a relative improvement of 7.84% in NDCG over the best baseline methods. This indicates a notable enhancement in the model's ability to accurately rank user preferences for items.

The experiments demonstrate CoNet's capability to effectively use fewer training examples compared to non-transfer learning methods while maintaining competitive performance. This efficiency indicates the potential of CoNet in reducing the data collection costs, which is particularly advantageous in data-scarce environments.

Implications and Future Work

The implications of this research extend to both theoretical and practical dimensions of AI development, particularly in the field of recommendation systems. CoNet provides a promising path for exploiting cross-domain relationships without extensive domain-specific feature engineering, highlighting the power of deep learning architectures in learning complex interaction functions.

From a practical standpoint, CoNet's ability to alleviate data sparsity through effective knowledge transfer could make it particularly useful for industrial applications where user interaction data can be highly fragmented across different domains.

Future developments might explore hybrid models that integrate content information alongside collaborative filtering techniques, potentially further alleviating limitations such as the cold-start problem. Additionally, there remains room to investigate more sophisticated transfer learning strategies tailored to domain-specific nuances, which could further improve cross-domain knowledge integration.

In conclusion, the CoNet framework offers a valuable contribution to the field of cross-domain recommendation by combining the strengths of deep learning with transfer learning to address challenges inherent in data sparsity, thereby setting a precedent for future exploration in this space.