Neural Cross-Domain Collaborative Filtering with Shared Entities
This addresses data sparsity and cold-start issues in recommendation systems, but it is incremental as it builds on existing cross-domain collaborative filtering methods.
The paper tackles data sparsity and cold-start problems in cross-domain recommendation systems by proposing NeuCDCF, an end-to-end neural network model that combines matrix factorization and deep neural networks, and it outperforms state-of-the-art models on four real-world datasets.
Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model -- NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide and deep framework and it learns the representations combinedly from both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.