CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

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.

arXiv commentDeep transfer learning for recommender systems

References in corpus (1)

• Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Cited by in corpus (24)

• Contrastive Cross-Domain Sequential Recommendation
• Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
• RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
• Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
• Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
• HAMUR: Hyper Adapter for Multi-Domain Recommendation
• Cross-Market Product Recommendation
• Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
• Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
• Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
• Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
• ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
• A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot Transfer
• Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
• Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
• In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
• A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning
• Market-Aware Models for Efficient Cross-Market Recommendation
• Review-Based Hyperbolic Cross-Domain Recommendation
• Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling
• Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
• Improving conversion rate prediction via self-supervised pre-training in online advertising
• Dual prototype attentive graph network for cross-market recommendation