Separated Graph Neural Networks for Recommendation Systems

Sun, Jianwen; Gao, Lu; Shen, Xiaoxuan; Liang, Ruxia; Du, Shangheng; Liu, Shengyingjie

doi:10.1109/tii.2022.3194659

Cited by 14 publications

(2 citation statements)

References 31 publications

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“…Recommendation systems are pivotal in navigating the vast amount of content available in today's digital age, enhancing user experience by personalizing content delivery based on user preferences and behaviors. The evolution of recommendation systems has been marked by significant advancements, from basic collaborative filtering algorithms to complex deep learning-based models [1,2].…”

Section: Recommendation Systemmentioning

confidence: 99%

An Effective Federated Recommendation Framework with Differential Privacy

Xu,

Chu,

Song

2024

Electronics

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This paper proposes a novel federated recommendation framework that incorporates differential privacy to safeguard user privacy without compromising on the accuracy of recommendations. Unlike conventional recommendation systems that centralize user data, leading to potential privacy breaches, our framework ensures that user data remain on local devices. It leverages a federated learning approach, where a global model is trained across multiple devices without exchanging raw data. To enhance privacy protection, we integrate a specially designed differential privacy algorithm that adds carefully calibrated noise to the aggregated data updates. This mechanism ensures that the global model cannot be exploited to infer individual user information. We evaluate our framework on two real-world datasets, one from the e-commerce sector and another from the multimedia content recommendation domain. The results exhibit that our framework achieves competitive recommendation accuracy compared to traditional centralized approaches, with minimal loss in precision and recall metrics, while significantly enhancing user privacy. Our work stands as a testament to the feasibility of creating recommendation systems that do not have to choose between privacy and performance, paving the way for more ethical AI applications in sensitive domains.

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Section: Recommendation Systemmentioning

confidence: 99%

An Effective Federated Recommendation Framework with Differential Privacy

Xu,

Chu,

Song

2024

Electronics

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“…Hybrid recommendation models primarily aim to alleviate sparsity issues by integrating information from content-based filtering and collaborative filtering models. Moreover, there exist various techniques for combining recommendation models with other methodologies such as data mining [46], KNN (k-nearest neighbors) [47], clustering [48], neural networks [49], and others. The integration of novel algorithms into recommendation systems contributes to their performance enhancement and broadens their development prospects.…”

Section: Recommendation Modelsmentioning

confidence: 99%

Recommendation Systems for the Metaverse

Wei,

Wang,

Wang

et al. 2023

Blockchains

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The metaverse, a virtual world closely intertwined with reality, represents the next-generation form of the Internet and encompasses advanced sociability and interaction. With the rapid growth of users and virtual products within this environment, the importance of recommendation systems becomes paramount. This article presents a comprehensive survey that examines the technologies influencing the development of recommendation systems tailored for the metaverse. Firstly, we identify the key trends and fundamental concepts associated with these systems. Secondly, we delve into the core supporting technologies and application scenarios within metaverse recommendation systems. Lastly, we provide insights into future research directions for this rapidly evolving field. Our survey reveals that while progress has been achieved, further investigation is necessary to optimize the technical layout and content of metaverse recommendation systems.

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Refined Node Type Graph Convolutional Network for Recommendation

He,

Sun,

et al. 2023

Lecture Notes in Computer Science

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Separated Graph Neural Networks for Recommendation Systems

Cited by 14 publications

References 31 publications

An Effective Federated Recommendation Framework with Differential Privacy

An Effective Federated Recommendation Framework with Differential Privacy

Recommendation Systems for the Metaverse

Refined Node Type Graph Convolutional Network for Recommendation

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