CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
This work addresses data sparsity in recommender systems for users and platforms by enabling more efficient training with fewer examples, though it is incremental as it builds on existing transfer learning methods.
The authors tackled the data sparsity problem in recommender systems by proposing CoNet, a deep transfer learning approach for cross-domain recommendation that uses collaborative cross networks to enable dual knowledge transfer, achieving a 7.84% relative improvement in NDCG over baselines on real-world datasets.
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.