Recommendation via Collaborative Diffusion Generative Model

Walker, Joojo; Zhong, Ting; Zhang, Fengli; Gao, Qiang; Zhang, Fan

doi:10.1007/978-3-031-10989-8_47

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…In this section, we present the Time-Interval Diffusion Recommendation model (TI-DiffRec). Following the classical DM methods (Ho, Jain, and Abbeel 2020;Nichol and Dhariwal 2021), the pioneering methods (Walker et al 2022;Wang et al 2023) typically leverage the original interaction matrix for diffusion, which is challenging to apply in SR. Despite incorporating the temporal order of user interactions, T-DiffRec (Wang et al 2023) overlooks the time interval between consecutive behaviors, potentially leading to the issue of preference drift.…”

Section: Time-interval Diffusion Modelmentioning

confidence: 99%

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Plug-In Diffusion Model for Sequential Recommendation

Ma,

Xie,

Meng

et al. 2024

AAAI

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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.

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Section: Time-interval Diffusion Modelmentioning

confidence: 99%

“…Recently, CODIGEM (Walker et al 2022) and DiffRec (Wang et al 2023) have introduced DM into recommendation, which generates users' preferences based on their historical behaviors, yielding promising results. However, these pioneering studies still grapple with two challenges:…”

Section: Introductionmentioning

confidence: 99%

Plug-In Diffusion Model for Sequential Recommendation

Ma,

Xie,

Meng

et al. 2024

AAAI

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“…However, VAEs often fail to effectively capture personalized user preferences, and GANs suffer from training instability. To overcome these limitations, Walker et al [22] were the first to apply the DM to recommender systems, offering superior representational capabilities and training stability. Through the DM, they leveraged robust collaborative signals and latent representations of user-item interactions, resulting in improved performance compared to previous VAE-based models.…”

Section: Related Workmentioning

confidence: 99%

EDiffuRec: An Enhanced Diffusion Model for Sequential Recommendation

Lee,

Kim

2024

Mathematics

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Sequential recommender models should capture evolving user preferences over time, but there is a risk of obtaining biased results such as false positives and false negatives due to noisy interactions. Generative models effectively learn the underlying distribution and uncertainty of the given data to generate new data, and they exhibit robustness against noise. In particular, utilizing the Diffusion model, which generates data through a multi-step process of adding and removing noise, enables stable and effective recommendations. The Diffusion model typically leverages a Gaussian distribution with a mean fixed at zero, but there is potential for performance improvement in generative models by employing distributions with higher degrees of freedom. Therefore, we propose a Diffusion model-based sequential recommender model that uses a new noise distribution. The proposed model improves performance through a Weibull distribution with two parameters determining shape and scale, a modified Transformer architecture based on Macaron Net, normalized loss, and a learning rate warmup strategy. Experimental results on four types of real-world e-commerce data show that the proposed model achieved performance gains ranging from a minimum of 2.53% to a maximum of 13.52% across HR@K and NDCG@K metrics compared to the existing Diffusion model-based sequential recommender model.

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“…First, a group of works [144,151] aims to learn users' future interaction probabilities through diffusion models. For example, DiffRec [151] predicts users' future interactions using corrupted noises from the users' historical interactions.…”

Section: Diffusion Modelsmentioning

confidence: 99%

A Review of Modern Fashion Recommender Systems

Deldjoo,

Nazary,

Ramisa

et al. 2023

ACM Comput. Surv.

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The textile and apparel industries have grown tremendously over the last few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion RS can have a noticeable impact on billions of customers’ shopping experiences and increase sales and revenues on the provider side. The goal of this survey is to provide a review of recommender systems that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, explainability, among others) and type of side-information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.

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Recommendation via Collaborative Diffusion Generative Model

Cited by 8 publications

References 14 publications

Plug-In Diffusion Model for Sequential Recommendation

Plug-In Diffusion Model for Sequential Recommendation

EDiffuRec: An Enhanced Diffusion Model for Sequential Recommendation

A Review of Modern Fashion Recommender Systems

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