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A Directional Diffusion Graph Transformer for Recommendation (2404.03326v1)

Published 4 Apr 2024 in cs.IR

Abstract: In real-world recommender systems, implicitly collected user feedback, while abundant, often includes noisy false-positive and false-negative interactions. The possible misinterpretations of the user-item interactions pose a significant challenge for traditional graph neural recommenders. These approaches aggregate the users' or items' neighbours based on implicit user-item interactions in order to accurately capture the users' profiles. To account for and model possible noise in the users' interactions in graph neural recommenders, we propose a novel Diffusion Graph Transformer (DiffGT) model for top-k recommendation. Our DiffGT model employs a diffusion process, which includes a forward phase for gradually introducing noise to implicit interactions, followed by a reverse process to iteratively refine the representations of the users' hidden preferences (i.e., a denoising process). In our proposed approach, given the inherent anisotropic structure observed in the user-item interaction graph, we specifically use anisotropic and directional Gaussian noises in the forward diffusion process. Our approach differs from the sole use of isotropic Gaussian noises in existing diffusion models. In the reverse diffusion process, to reverse the effect of noise added earlier and recover the true users' preferences, we integrate a graph transformer architecture with a linear attention module to denoise the noisy user/item embeddings in an effective and efficient manner. In addition, such a reverse diffusion process is further guided by personalised information (e.g., interacted items) to enable the accurate estimation of the users' preferences on items. Our extensive experiments conclusively demonstrate the superiority of our proposed graph diffusion model over ten existing state-of-the-art approaches across three benchmark datasets.

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References (61)
  1. How expressive are graph neural networks in recommendation. In Proc. of CIKM.
  2. D4Explainer: In-distribution GNN explanations via discrete denoising diffusion. arXiv preprint arXiv:2310.19321 (2023).
  3. Yifan Chen and Maarten de Rijke. 2018. A collective variational autoencoder for top-n recommendation with side information. In Proc. of DLRS.
  4. Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. In Proc. of NeurIPS.
  5. Diffuser: efficient transformers with multi-hop attention diffusion for long sequences. In Proc. of AAAI.
  6. Graph neural networks for recommender system. In Proc. of WSDM.
  7. Diffusion improves graph learning. In Proc. of NeurIPS.
  8. Diffusion models for graphs benefit from discrete state spaces. In Proc. of LoG.
  9. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proc. of SIGIR.
  10. Session-based recommendations with recurrent neural networks. In Proc. of ICLR.
  11. Denoising diffusion probabilistic models. In Proc. of NeurIPS.
  12. MUDiff: Unified diffusion for complete molecule generation. arXiv preprint arXiv:2304.14621 (2023).
  13. Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. Transactions on Interactive Intelligent Systems (2014).
  14. Adaptive graph contrastive learning for recommendation. In Proc. of SIGKDD.
  15. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proc. of ICDM.
  16. Autoregressive diffusion model for graph generation. In Proc. of ICML.
  17. Graph transformer for recommendation. Proc. of SIGIR (2023).
  18. DiffuRec: A Diffusion Model for Sequential Recommendation. arXiv preprint arXiv:2304.00686 (2023).
  19. Variational autoencoders for collaborative filtering. In Proc. of WebConf.
  20. Diffusion augmentation for sequential recommendation. In Proc. of CIKM.
  21. Recommender systems with heterogeneous side information. In Proc. of WebConf.
  22. Image-based recommendations on styles and substitutes. In Proc. of SIGIR.
  23. Graph neural pre-training for enhancing recommendations using side information. Transitions on Information Systems (2021).
  24. Neural anisotropy directions. In Proc. of NeurIPS.
  25. Are my deep learning systems fair? An empirical study of fixed-seed training. In Proc. of NeuIPS.
  26. High-resolution image synthesis with latent diffusion models. In Proc. of the CVPR.
  27. Donald J Schuirmann. 1987. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of pharmacokinetics and biopharmaceutics 15 (1987).
  28. Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In Proc. of WSDM.
  29. Deep unsupervised learning using nonequilibrium thermodynamics. In Proc. of ICML.
  30. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proc. of CIKM.
  31. Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization. Image and Vision Computing 55 (2016), 119–126.
  32. Attention is all you need. Proc. of NeurIPS (2017).
  33. DiGress: Discrete denoising diffusion for graph generation. In Proc. of ICLR.
  34. Recommendation via collaborative diffusion generative model. In Proc. of KSEM.
  35. Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020).
  36. Denoising implicit feedback for recommendation. In Proc. of WSDM. 373–381.
  37. Diffusion Recommender Model. Proc. of SIGIR.
  38. KGAT: Knowledge graph attention network for recommendation. In Proc. of SIGKDD.
  39. Neural graph collaborative filtering. In Proc. of SIGIR.
  40. Learning intents behind interactions with knowledge graph for recommendation. In Proc. of WWW.
  41. Max Welling and Thomas N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In Proc. of ICLR.
  42. Mind: A large-scale dataset for news recommendation. In Proc. of ACL.
  43. Self-supervised graph learning for recommendation. In Proc. of SIGIR.
  44. Difformer: Scalable (graph) transformers induced by energy constrained diffusion. arXiv preprint arXiv:2301.09474 (2023).
  45. Collaborative denoising auto-encoders for top-n recommender systems. In Proc. of WSDM.
  46. Linear discriminant analysis. Robust data mining (2013), 27–33.
  47. Directional diffusion models for graph representation learning. Proc. of NeurIPS (2023).
  48. Knowledge graph contrastive learning for recommendation. In Proc. of SIGIR.
  49. Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation. Transactions on Information Systems (2023).
  50. Large multi-modal encoders for recommendation. arXiv preprint arXiv:2310.20343 (2023).
  51. Contrastive Graph Learning with Positional Representation for Recommendation. (2023).
  52. Contrastive graph prompt-tuning for cross-domain recommendation. ACM Transactions on Information Systems 42, 2 (2023), 1–28.
  53. Graph contrastive learning with positional representation for recommendation. In European Conference on Information Retrieval. Springer, 288–303.
  54. Multi-modal graph contrastive learning for micro-video recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1807–1811.
  55. Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In Proc. of SIGIR.
  56. Self-supervised learning for recommender systems: a survey. In Proc. of SIGIR.
  57. Collaborative knowledge base embedding for recommender systems. In Proc. of SIGKDD.
  58. SLED: Structure learning based denoising for recommendation. Transactions on Information Systems (2023).
  59. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proc. of CIKM.
  60. Multi-level cross-view contrastive learning for knowledge-aware recommender system. In Proc. of SIGIR.
  61. Diffusion models in nlp: A survey. arXiv preprint arXiv:2305.14671 (2023).
Citations (6)
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Summary

  • The paper introduces DiffGT, which employs a novel diffusion process with directional noise and a graph transformer to effectively denoise noisy user-item interactions.
  • It utilizes a linear attention module to reduce computational complexity while preserving item heterogeneity and capturing nuanced user preferences.
  • Extensive experiments show DiffGT outperforms ten state-of-the-art models across benchmark datasets, setting a new standard in recommender systems.

A Novel Approach for Recommender Systems: The Diffusion Graph Transformer

Introduction

Recommender systems are integral in navigating the vast amount of content available online, from movies and music to products and services. The Diffusion Graph Transformer (DiffGT) model, introduced by Zixuan Yi, Xi Wang, and Iadh Ounis, marks a significant advancement in the field of recommendation systems. Their work seeks to address the critical issue of noisy data in user-item interactions, a common challenge that hampers the ability of traditional graph neural recommenders to accurately capture user preferences.

The Diffusion Graph Transformer Model

Addressing Noisy Data Through Diffusion

Implicit interactions, such as clicks or views, are the cornerstone of collaborative filtering techniques in recommender systems. However, these interactions are often riddled with noise, falsely representing user preferences. DiffGT introduces an innovative diffusion process comprising a forward phase to gradually introduce noise to implicit interactions, followed by a reverse process that iteratively refines user hidden preferences through denoising. This approach deviates from the isotropic Gaussian noises used in existing models, adopting anisotropic and directional Gaussian noises that better represent the inherent structure of user-item interaction graphs.

Leveraging Directional Noise

The proposed model utilizes directional noise in its forward diffusion process, aligning with the observation that recommendation data often exhibit anisotropic structures. This strategic application of noise enhances the model's ability to retain item heterogeneity and accurately capture the nuances of user preferences through the diffusion process.

Transformer Architecture for Denoising

In the reverse diffusion phase, DiffGT employs a graph transformer architecture combined with a linear attention module. This design efficiently denoises the noisy user/item embeddings, leveraging personalized information to guide the denoising process accurately. The integration of a linear transformer demonstrates the model's ability to address the computational complexity typically associated with traditional transformer models in denoising tasks.

Underlying Mechanisms and Efficacy

The DiffGT model’s effectiveness is evident from extensive experiments that demonstrate its superiority over ten state-of-the-art approaches across three benchmark datasets. The model's success can be primarily attributed to the novel incorporation of directional noise and the adept use of a linear transformer in the diffusion process. These innovations allow for a nuanced understanding and processing of noisy data, enabling more accurate and user-tailored recommendations.

Theoretical and Practical Implications

The introduction of DiffGT offers both theoretical advancements and practical improvements in the field of recommender systems. Theoretically, the model presents a novel application of diffusion processes paired with directional noise and transformer architecture in addressing data noise - a pervasive issue in collaborative filtering. Practically, DiffGT’s framework provides a scalable and efficient solution for enhancing recommendation accuracy in real-world systems, potentially improving user satisfaction and engagement.

Future Directions in AI and Recommender Systems

The DiffGT model opens new avenues for future research and development in AI-based recommender systems. Expanding the application of diffusion processes with directional noise beyond graph neural recommenders to other domains, such as sequential recommendation or knowledge graph-enhanced recommendation, presents a promising frontier. Additionally, exploring the integration of more diverse data types and leveraging advanced attention mechanisms could further refine and enhance the capabilities of recommender systems, pushing the boundaries of personalized content delivery.

Conclusion

The Diffusion Graph Transformer model signifies a pivotal step forward in the evolution of recommender systems. By effectively addressing the challenge of noisy user-item interactions through directional noise and a graph transformer architecture, DiffGT sets a new benchmark for accuracy and efficiency in recommendations. As we move forward, the principles and innovations introduced by this model are likely to influence the development of more advanced, accurate, and user-centric recommender systems across various digital platforms.

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