# JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

arXiv: https://arxiv.org/abs/1910.08219 · HTML: https://mlsanity.com/paper/1910.08219/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN](https://mlsanity.com/paper/2108.11883/) (2021)
- [Pre-training Graph Neural Network for Cross Domain Recommendation](https://mlsanity.com/paper/2111.08268/) (2021)
- [Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems](https://mlsanity.com/paper/2106.04993/) (2021)
- [Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization](https://mlsanity.com/paper/2306.13050/) (2023)
- [Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling](https://mlsanity.com/paper/2502.16239/) (2025)

## Abstract (arXiv)

Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping. The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art methods. Our code is available online ~footnotehttps://github.com/JimLiu96/JSCN.