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 .

Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users’ interests are often personalized, meta-learning can be used to learn users’ personalized preferences, and personalized preferences can help models make recommendations in cold-start scenarios. We use meta-learning to learn the personalized preferences of cold-start users. Therefore, we offer a unique meta-learning-based federated personalized cross-domain recommendation model that discovers the personalized preferences for cold-start users via a server-side meta-recommendation module. To avoid compromising user privacy, an attention mechanism is used on each client to find transferable features that contribute to knowledge transfer while obtaining embeddings of users and items; each client then uploads the weights to the server. The server accumulates weights and delivers them to clients for update. Compared to traditional recommendation system models, our model can effectively protect user privacy while solving the user cold-start problem, as we use an attention mechanism in the local embedding module to mine the source domain for transferable features that contribute to knowledge transfer. Extensive trials on real-world datasets have demonstrated that our technique effectively guarantees speed while protecting user privacy.