# Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

arXiv: https://arxiv.org/abs/1901.07199 · HTML: https://mlsanity.com/paper/1901.07199/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

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

- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection](https://mlsanity.com/paper/2201.01004/) (2022)
- [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation](https://mlsanity.com/paper/2101.05611/) (2021)
- [GReS: Graphical Cross-domain Recommendation for Supply Chain Platform](https://mlsanity.com/paper/2209.01031/) (2022)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)

## Abstract (arXiv)

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.