# Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation

arXiv: https://arxiv.org/abs/2202.04920 · HTML: https://mlsanity.com/paper/2202.04920/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1803.10081/) (2018)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [GOT: An Optimal Transport framework for Graph comparison](https://mlsanity.com/paper/1906.02085/) (2019)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1808.09347/) (2018)
- [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance](https://mlsanity.com/paper/2005.10545/) (2020)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems](https://mlsanity.com/paper/1903.10794/) (2019)
- [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021)
- [Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation](https://mlsanity.com/paper/2007.07085/) (2020)
- [Multi-Facet Recommender Networks with Spherical Optimization](https://mlsanity.com/paper/2103.14866/) (2021)

## Built on by (most cited first, 14 of 14)

- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [TopoFR: A Closer Look at Topology Alignment on Face Recognition](https://mlsanity.com/paper/2410.10587/) (2024)
- [DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2209.10163/) (2022)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Federated Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2410.08249/) (2024)
- [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://mlsanity.com/paper/2308.05508/) (2023)
- [Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering](https://mlsanity.com/paper/2305.16335/) (2023)
- [Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation](https://mlsanity.com/paper/2501.11671/) (2025)
- [Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2308.11646/) (2023)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach](https://mlsanity.com/paper/2406.16494/) (2024)
- [Enhancing CTR Prediction in Recommendation Domain with Search Query Representation](https://mlsanity.com/paper/2410.21487/) (2024)
- [FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations](https://mlsanity.com/paper/2505.18177/) (2025)

## Abstract (arXiv)

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem. The problem is commonly-existed and challenging due to two main aspects, i.e, there are only positive user-item ratings on the target domain and there is no overlapped user across different domains. Most previous CDR approaches cannot solve the RNCDR problem well, since (1) they cannot effectively combine review with other information (e.g., ID or ratings) to obtain expressive user or item embedding, (2) they cannot reduce the domain discrepancy on users and items. To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. CFAA includes two main modules, i.e., rating prediction module and embedding attribution alignment module. The former aims to jointly mine review, one-hot ID, and multi-hot historical ratings to generate expressive user and item embeddings. The later includes vertical attribution alignment and horizontal attribution alignment, tending to reduce the discrepancy based on multiple perspectives. Our empirical study on Douban and Amazon datasets demonstrates that CFAA significantly outperforms the state-of-the-art models under the RNCDR setting.