Abstract
The effectiveness of Knowledge Graphs (KGs) in enhancing recommendation systems has been recognized. However, the effectiveness of KG-enhanced recommendations is often hampered by issues of entity sparsity and noise. To address these challenges, we propose a Diffusion-based Contrastive Learning with Knowledge Graphs for Recommendation (DICES). Our method combines diffusion models with multi-level contrastive learning approaches, aiming to enhance the performance of existing recommendation systems. By utilizing diffusion models, we ensure that the generated augmented samples are context-aware, thereby increasing the robustness of contrastive learning. Additionally, we introduce a multi-level contrastive learning approach to improve recommendation accuracy. Finally, we design a joint training framework to optimize both the recommendation task and the multi-level contrastive learning tasks, further enhancing the overall effectiveness of the recommendation system. Extensive experiments on multiple benchmark datasets demonstrate that our DICES framework significantly outperforms existing state-of-the-art methods in scenarios with sparse user-item interactions and noisy KG data.
H. Dong and H. Liang—Contributed equally to this work.
Corresponding authors: Jing Yu (yujing02@iie.ac.cn).
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Acknowledgements
This work is partially supported by the National Key Research and Development Program of China (Grant No. 2021YFB2701300), the National Natural Science Foundation of China (Grant No. 62372044), and the Open Topics of Key Laboratory of Blockchain Technology and Data Security, The Ministry of Industry and Information Technology of the People’s Republic of China(Grant No. 20242217).
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Dong, H., Liang, H., Yu, J., Gai, K. (2024). DICES: Diffusion-Based Contrastive Learning with Knowledge Graphs for Recommendation. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14885. Springer, Singapore. https://doi.org/10.1007/978-981-97-5495-3_9
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