# Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering

## TL;DR

This paper introduces ADC, an adaptive deep learning approach for cross-domain recommendation that dynamically balances domain contributions and transfers user behavior knowledge across multiple platforms.

## Contribution

It proposes a novel neural architecture with a cross-domain loss function and an efficient algorithm for balancing domain influences during training.

## Key findings

- ADC outperforms state-of-the-art methods on six datasets.
- The adaptive strategy effectively balances domain complexities.
- The model improves recommendation accuracy across diverse domains.

## Abstract

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. In this paper, we propose an adaptive deep learning strategy for cross-domain recommendation, referred to as ADC. We design a neural architecture and formulate a cross-domain loss function, to compute the non-linearity in user preferences across domains and transfer the knowledge of users' multiple behaviors, accordingly. In addition, we introduce an efficient algorithm for cross-domain loss balancing which directly tunes gradient magnitudes and adapts the learning rates based on the domains' complexities/scales when training the model via backpropagation. In doing so, ADC controls and adjusts the contribution of each domain when optimizing the model parameters. Our experiments on six publicly available cross-domain recommendation tasks demonstrate the effectiveness of the proposed ADC model over other state-of-the-art methods. Furthermore, we study the effect of the proposed adaptive deep learning strategy and show that ADC can well balance the impact of the domains with different complexities.