Plug-in Diffusion Model for Sequential Recommendation (2401.02913v1)

Abstract: Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

• The paper introduces PDRec, a framework that integrates diffusion models into sequential recommendation tasks to overcome data sparsity.
• It proposes innovative modules like TI-DiffRec for temporal reweighting and HBR for refining historical interactions, resulting in significant performance gains.
• Experimental results show that PDRec outperforms baseline models across various encoders and datasets, demonstrating its adaptability and real-world potential.

An Analysis of the Plug-in Diffusion Model for Sequential Recommendation

The paper presents a novel approach to improving sequential recommendation tasks by leveraging the powerful generalization capabilities of diffusion models, termed the Plug-in Diffusion Model for Recommendation (PDRec). This research proposes a framework that integrates diffusion models into recommendation systems, particularly aiming to alleviate the challenges posed by data sparsity. The framework is characterized by its adaptability and effectiveness, enabling it to be utilized with various sequential recommendation models and tasks.

The primary motivation of this work stems from the limitations observed in existing recommendation strategies that typically rely on the highest-score items for predicting user interests. Such approaches often neglect the comprehensive preferences encapsulated within lower-scored items, thereby struggling with data sparsity issues. PDRec addresses these challenges by adopting a diffusion model as a flexible plugin to infer user preferences on all items, thereby maximizing the potential information derived from the user data corpus.

Core Contributions

1. Time-Interval Diffusion Model (TI-DiffRec): The authors introduce an enhanced diffusion model that incorporates time-interval reweighting to better capture the temporal dynamics of user interactions. This model facilitates a more precise generation of user preferences by considering time intervals between consecutive behaviors, thus addressing the issue of preference drift.
2. Historical Behavior Reweighting (HBR): PDRec utilizes an HBR mechanism that leverages diffusion-based preference outputs to reweight historical user behaviors. This effectively distinguishes high-quality behaviors from noise, ensuring that model optimization is both accurate and efficient.
3. Diffusion-based Positive Augmentation (DPA): Recognizing the potential insights from unobserved user preferences, PDRec employs self-distillation to treat top-ranked unobserved items as potential positive engagements. This strategy is pivotal in counteracting the intrinsic data sparsity problems.
4. Noise-free Negative Sampling (NNS): The introduction of a noise-free negative sampling technique ensures the selection of stable negative samples. This mitigates the risk of false negative sampling, maintaining effective model optimization.

Experimental Validation and Results

The experimental results of PDRec reveal its superiority over existing baseline models across multiple datasets and sequential encoders like GRU4Rec, SASRec, and CL4SRec. PDRec consistently achieves significant improvements, demonstrating its utility as a versatile framework that can be easily adapted to various recommendation scenarios. Furthermore, analyses confirm that PDRec's components, such as HBR, DPA, and NNS, are effective in isolating and leveraging critical user preference signals.

The paper also extends the application of PDRec to cross-domain sequential recommendation tasks, exemplifying its broader applicability and robustness.

Implications and Future Directions

The introduction of PDRec highlights the potential of diffusion models in enhancing recommendation system performance through comprehensive preference modeling. The framework's adaptability suggests it could be extended to other domains involving sparse data scenarios. Future research could investigate advanced negative sampling strategies to further exploit diffusion-generated knowledge and explore other recommendation domains beyond sequential tasks, such as hybrid or content-based recommendation systems. Additionally, improving the computational efficiency of the model could facilitate its deployment in real-time recommendation environments.