# Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

arXiv: https://arxiv.org/abs/2007.07085 · HTML: https://mlsanity.com/paper/2007.07085/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2202.04920/) (2022)
- [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://mlsanity.com/paper/2405.14608/) (2024)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation](https://mlsanity.com/paper/2403.03600/) (2024)
- [Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation](https://mlsanity.com/paper/2501.11671/) (2025)
- [Few-shot News Recommendation via Cross-lingual Transfer](https://mlsanity.com/paper/2207.14370/) (2022)
- [Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation](https://mlsanity.com/paper/2110.12648/) (2021)
- [Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization](https://mlsanity.com/paper/2306.13050/) (2023)
- [FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations](https://mlsanity.com/paper/2505.18177/) (2025)
- [Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings](https://mlsanity.com/paper/2508.16210/) (2025)

## Abstract (arXiv)

Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples. However, this leads to a consequence that many potential positive samples are mislabeled as negative ones and data sparsity would exacerbate the mislabeling problem. To solve these difficulties, we regard the problem of recommendation on sparse implicit feedbacks as a semi-supervised learning task, and explore domain adaption to solve it. We transfer the knowledge learned from dense data to sparse data and we focus on the most challenging case -- there is no user or item overlap. In this extreme case, aligning embeddings of two datasets directly is rather sub-optimal since the two latent spaces encode very different information. As such, we adopt domain-invariant textual features as the anchor points to align the latent spaces. To align the embeddings, we extract the textual features for each user and item and feed them into a domain classifier with the embeddings of users and items. The embeddings are trained to puzzle the classifier and textual features are fixed as anchor points. By domain adaptation, the distribution pattern in the source domain is transferred to the target domain. As the target part can be supervised by domain adaptation, we abandon negative sampling in target dataset to avoid label noise. We adopt three pairs of real-world datasets to validate the effectiveness of our transfer strategy. Results show that our models outperform existing models significantly.