SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913819111981056 |
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| author | Zhao, Yuying Yang, Xiaodong Chen, Huiyuan Fan, Xiran Wang, Yu Cai, Yiwei Derr, Tyler |
| author_facet | Zhao, Yuying Yang, Xiaodong Chen, Huiyuan Fan, Xiran Wang, Yu Cai, Yiwei Derr, Tyler |
| contents | Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item embeddings that are crucial for recommendations. However, the data often suffers from sparsity and imbalance issues: limited observations of user-item interactions can result in sub-optimal performance, and a predominance of interactions with popular items may introduce recommendation bias. To address these challenges, we employ Pretrained Language Models (PLMs) to enhance the interaction data with textual information, leading to a denser and more balanced dataset. Specifically, we propose a simple yet effective data augmentation method (SimAug) based on the textual similarity from PLMs, which can be seamlessly integrated to any systems as a lightweight, plug-and-play component in the pre-processing stage. Our experiments across nine datasets consistently demonstrate improvements in both utility and fairness when training with the augmented data generated by SimAug. The code is available at https://github.com/YuyingZhao/SimAug. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation Zhao, Yuying Yang, Xiaodong Chen, Huiyuan Fan, Xiran Wang, Yu Cai, Yiwei Derr, Tyler Information Retrieval Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item embeddings that are crucial for recommendations. However, the data often suffers from sparsity and imbalance issues: limited observations of user-item interactions can result in sub-optimal performance, and a predominance of interactions with popular items may introduce recommendation bias. To address these challenges, we employ Pretrained Language Models (PLMs) to enhance the interaction data with textual information, leading to a denser and more balanced dataset. Specifically, we propose a simple yet effective data augmentation method (SimAug) based on the textual similarity from PLMs, which can be seamlessly integrated to any systems as a lightweight, plug-and-play component in the pre-processing stage. Our experiments across nine datasets consistently demonstrate improvements in both utility and fairness when training with the augmented data generated by SimAug. The code is available at https://github.com/YuyingZhao/SimAug. |
| title | SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.01695 |