# Neural Cross-Domain Collaborative Filtering with Shared Entities

## TL;DR

NeuCDCF is an end-to-end neural network model that combines matrix factorization and deep learning to improve cross-domain recommendation systems, effectively handling domain diversity and complex relationships.

## Contribution

The paper introduces NeuCDCF, a novel wide and deep neural network model that enhances cross-domain collaborative filtering by integrating multiple representation learning techniques.

## Key findings

- NeuCDCF outperforms existing CDCF models on four real-world datasets.
- The model effectively captures complex non-linear relationships across domains.
- NeuCDCF addresses diversity and data sparsity challenges in recommendation systems.

## Abstract

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.