The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Open accessAbstract
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git.
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Cites
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401063 · Cited by 4,430
Neural Graph Collaborative Filtering
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331267 · Cited by 3,823
Graph Neural Networks for Social Recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313488 · Cited by 1,984
Self-citationSelf-supervised Graph Learning for Recommendation
Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462862 · Cited by 1,488
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
He, R., & McAuley, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9973 · Cited by 992
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Fu, X., Zhang, J., Meng, Z., & King, I. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380297 · Cited by 976
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
Conference on Neural Information Processing Systems
Liu, L., Zhou, T., Long, G., Jiang, J., & Zhang, C. (2019) · UTS ePRESS (University of Technology Sydney) · Cited by 593
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Wei, Y., Wang, X., Nie, L., He, X., & Chua, T.-S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413556 · Cited by 373
Mining Latent Structures for Multimedia Recommendation
Zhang, J., Zhu, Y., Liu, Q., Wu, S., Wang, S., & Wang, L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475259 · Cited by 354
Cited by
A survey on large language models for recommendation
Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01291-2 · Cited by 515
Recommender Systems in the Era of Large Language Models (LLMs)
Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., Wen, Z., Wang, F., Zhao, X., Tang, J., et al. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3392335 · Cited by 481
Self-citationA survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges
Li, X., Wang, S., Zeng, S., Wu, Y., & Yang, Y. (2024) · Vicinagearth · doi:10.1007/s44336-024-00009-2 · Cited by 404
When large language models meet personalization: perspectives of challenges and opportunities
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367
“LLMRec [81] adopts LLM-based graph augmentation strategies to enrich the information of items and further designs denoise methods to ensure the augmentation.”
Large Language Models are Zero-Shot Rankers for Recommender Systems
Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-56060-6_24 · Cited by 327
Metadata
- Authors
- Wei, W., Ren, X., Tang, J., Wang, Q., Su, L., Cheng, S., Wang, J., Yin, D., & Huang, C.
- Venue
- Proceedings of the 17th ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2024-03-04
- Pages
- 806-815
- OpenAlex
- W4392367398
- Cited by
- 268
- References
- 31
- FWCI
- 105.33
- Type
- conference paper
- Language
- en
- Open access
- green
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