Sequential recommender systems often struggle with accurate personalized recommendations due to data sparsity issues. Existing works use variational autoencoders and generative adversarial network methods to enrich sparse data. However, they often overlook diversity in the latent data distribution, hindering the model’s generative capacity. This characteristic of generative methods can introduce additional noise in many cases. Moreover, retaining personalized user preferences through the generation process remains a challenge. This work introduces DGFedRS, a Federated Recommender System Based on Diffusion Augmentation and Guided Denoising, designed to capture the diversity in the latent data distribution while preserving user-specific information and suppressing noise. In particular, we pre-train the diffusion model using the recommender dataset and use a diffusion augmentation strategy to generate interaction sequences, expanding the sparse user-item interactions in the discrete space. To preserve user-specific preferences in the generated interactions, we employ a guided denoising strategy to guide the generation process during reverse diffusion. Subsequently, we design a noise control strategy to reduce the damage to personalized information during the diffusion process. Additionally, a stepwise scheduling strategy is devised to input generated data into the sequential recommender model based on their challenge levels. The success of the DGFedRS approach is demonstrated by thorough experiments conduct on three real-world datasets.

Electronic shopping’s convenience and efficiency make it essential in modern life. In trustworthy personalized consumer recommender scenarios, diverse consumer interests lead to various interactions. Existing methods struggle to capture complex behavior dependencies and shared information across behaviors by exploring multi-behavior interaction sequences. To address this, we propose the Personalized Consumer Federated Recommender System Using Fine-grained Transformation and Hybrid Information Sharing (PCFedRec). Our contributions are as follows: Firstly, we employ the Fine-grained Transformation Module to capture fine-grained heterogeneous dependencies of consumer behaviors and model the behavior semantics of interest. Secondly, we use the Weight Correction Mechanism to reduce suboptimal position encoding and noisy input. Finally, we design the Hybrid Information Sharing Module to learn general consumer interests by leveraging common information across different behaviors. The Normalized Discounted Cumulative Gain is used to evaluate the performance of the recommender systems by considering both the relevance and rank order of the top 10 recommended items. The PCFedRec improves performance by 6.20%, 8.08%, 9.16%, and 7.14% on this metric.

The field of recommender systems aims to predict users’ latent interests by analyzing their preferences and behaviors. However, privacy concerns about user data collection lead to challenges such as incomplete initial information and data sparsity. Federated learning has emerged to address these privacy issues in recommender systems. However, federated recommender systems face heterogeneity among edge devices regarding data features and sample sizes. Moreover, differences in computational and storage capabilities introduce communication overhead and processing delays during parameter aggregation at the third-party server. This article introduces a framework named FedRL , a reinforcement learning federated recommender system for efficient communication using Reinforcement Selector and Hypernet Generator, to address the proposed issues. The Reinforcement Selector dynamically selects participating edge devices and helps to maximize their use of local data resources. Meanwhile, Hypernet Generator optimizes communication bandwidth consumption during the federated learning parameter transmission, enabling rapid deployment and updates of new model architectures or hyperparameters. Furthermore, the framework incorporates item attributes as content embeddings in edge devices’ recommender models, enriching them with global information. Real-world dataset experiments demonstrate that the proposed solution balances recommender quality and communication efficiency. The code for this work is publicly available on GitHub: https://github.com/diyicheng/FedRL .

Raining is a common weather condition, and may seriously degrade the performances of outdoor computer vision systems, such as surveillance and autonomous navigation. Rain streaks may exhibit diverse appearances in the captured images, depending on their distances from the camera. For example, sparse rain streaks near the camera lens may appear as continuous and translucent strips, while distant densely accumulated rain streaks are more like fog and mist. Existing rain removal methods are mainly based on a single input image. However, on a single image, it is difficult to estimate a reliable depth map for rain removal. A light field image (LFI) records abundant structural and texture information of the target scene by capturing multi-perspective sub-aperture views with a single exposure. With a LFI, it is easier to estimate the depth maps, and rain streak locations across sub-aperture views are highly correlated. We observe that rain streaks usually have different slops and/or chromaic values, compared with the background scene, along the epipolar plane images (EPIs) of an LFI. Thus, we propose to make use of 3D EPIs to detect rain streaks and restore the background. To this end, we propose a novel GAN architecture to remove rain streaks from an LFI. Our method takes as input a 3D EPI, i.e., a stacked of sub-aperture views along the same row of a rainy LFI. It first estimates the disparity maps for the 3D EPI by utilizing an auto-encoder based depth estimation sub-network. The disparity maps concatenated with the input sub-aperture views are then fed into a non-local residual block, and two branched autoencoder sub-networks are used to extract rain-streaks and recover rain-free sub-aperture views. Extensive experiments conducted on both synthetic real-world-like LFIs and real-world LFIs demonstrate the effectiveness of our method.