NSTL国家科技图书文献中心

1. Federated Recommender System Based on Diffusion Augmentation and Guided Denoising NSTL国家科技图书文献中心

YICHENG DI | HONGJIAN SHI ... - 《ACM transactions on information systems》 - 2025,43(2) - 31.1～31.36 - 共36页 - 被引量：2

摘要： Recommender System Based on Diffusion Augmentation and | Sequential recommender* systems often struggle* | recommender dataset and use a diffusion augmentation | sequential recommender* model based on their challenge* | with accurate personalized recommendations due to

关键词： Diffusion Model | Data Augmentation | Federated Recommender System | Guided Denoising

2. PDC-FRS: Privacy-Preserving Data Contribution for Federated Recommender System NSTL国家科技图书文献中心

Chaoqun Yang | Wei Yuan ... - 《Advanced Data Mining and Applications,Part VI》 - International Conference on Advanced Data Mining and Applications - 2025, - 65～79 - 共15页

摘要：Federated recommender* systems (FedRecs) have* | emerged as a popular research direction for protecting | users' privacy in on-device recommendations. In | FedRecs, users keep their data locally and only | contribute their local collaborative information by

关键词： Recommender system | Federated learning | Privacy protection

3. SiSRS: Signed social recommender system using deep neural network representation learning NSTL国家科技图书文献中心

Abed Heshmati | Majid Meghdadi ... - 《Expert Systems with Application》 - 2025,259(Jan.) - 125205.1～125205.15 - 共15页

摘要：, in this paper, a signed social recommender system | challenges in developing signed social recommender systems | Most approaches used in social recommender | systems concentrate mostly on positive relationships | within the social network, frequently ignoring the

关键词： Deep learning | Signed social recommender system | Network representation learning | Collaborative filtering

4. AIREG: Enhanced Educational Recommender System with Large Language Models and Knowledge Graphs NSTL国家科技图书文献中心

Fatemeh Fathi - 《The Semantic Web: ESWC 2024 Satellite Events,Part II》 - European Semantic Web Conference - 2025, - 84～93 - 共10页

摘要： advancements in knowledge-enhanced Recommender Systems | Systems to propose a novel system, named AIREG for the | system with explicit factual knowledge from KGs. This | developments in knowledge-enhanced conversational Recommender | In the nowadays modern digital era, the

关键词： Recommender system | Large language model | Knowledge graph | Knowledge extraction

Longchuan Tu | Shunmei Meng ... - 《Web Information Systems Engineering - WISE 2024,Part III》 - International Conference on Web Information Systems Engineering - 2025, - 17～31 - 共15页

摘要：Recently, graph neural networks (GNNs) have | played a key crucial in many recommendation situations | . In particular, contrastive learning-based | hypergraph neural networks (HGNNs) are gradually becoming a | research focus for addressing issues of data sparsity and

关键词： Recommender system | Graph neural network | Hypergraph learning | Contrastive learning | Hypergraph structure

6. Nipping trouble in the bud: A proactive tourism recommender system NSTL国家科技图书文献中心

Li C. | Zheng W. - 《Information & management》 - 2025,62(1) - 1.1～1.15 - 共15页

摘要： mechanisms into system* design, thereby enabling tourists to* | results suggest that our system* outperforms benchmarks* | system is also applicable for other predictable | © 2024 Elsevier B.V.Recommender systems have | been used extensively in tourism activities to aid

关键词： Crowding avoidance | Forecasting mechanism | Personalized tourist experience | Recommender system | Tourist trip design problem

7. PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System NSTL国家科技图书文献中心

WEI YUAN | CHAOQUN YANG ... - 《ACM transactions on information systems》 - 2025,43(2) - 52.1～52.24 - 共24页

摘要：Sequential recommender* systems, as a* | specialized branch of recommender* systems that can capture* | Sequential Recommender* Systems (FedSeqRecs), in which a* | public sequential recommender* model is shared and* | system users alike. Furthermore, since PTF-FSR only

关键词： Sequential Recommendation | Federated Learning | Contrastive Learning | Model Intellectual Property

8. Seeing Is Believing: The Role of Scatterplots in Recommender System Trust and Decision-Making NSTL国家科技图书文献中心

Bhavana Doppalapudi | Md Dilshadur Rahman ... - 《Advances in Visual Computing,Part I》 - International Symposium on Visual Computing - 2025, - 425～438 - 共14页

摘要： recommender system decisions. Our first study focuses on | recommender system to use. The second study focuses on low | The accuracy of recommender* systems influences* | decisions with recommender* systems is under-explored. To* | , validating decisions, and building trust in recommendation

关键词： Machine learning | Visualizations | Trust | Decision-Making

9. Recommender System-Induced Eating Disorder Relapse: Harmful Content and the Challenges of Responsible Recommendation NSTL国家科技图书文献中心

JENNIFER GOLBECK - 《ACM transactions on intelligent systems and technology》 - 2025,16(1) - 15.1～15.13 - 共13页

摘要： eating. However, if recommender* systems show them this* | intersection of recommender* systems, ED content, and users in* | As users social media feeds have become | increasingly driven by algorithmically recommended content | , there is a need to understand the impact these

关键词： Recommender systems | personalization | targeted advertising | eating disorders

10. User preference interaction fusion and swap attention graph neural network for recommender system NSTL国家科技图书文献中心

Li, Mingqi | Ma, Wenming ... - 《Neural Networks》 - 2025,184 - Article 107116～Article 107116 - 共23页

摘要：Recommender systems are widely used in various | applications. Knowledge graphs are increasingly used to | improve recommendation performance by extracting | valuable information from user-item interactions. However | , current methods do not effectively use fine-grained

摘要： Recommender System Based on Diffusion Augmentation and | Sequential recommender* systems often struggle* | recommender dataset and use a diffusion augmentation | sequential recommender* model based on their challenge* | with accurate personalized recommendations due to

关键词： Diffusion Model | Data Augmentation | Federated Recommender System | Guided Denoising

摘要：Federated recommender* systems (FedRecs) have* | emerged as a popular research direction for protecting | users' privacy in on-device recommendations. In | FedRecs, users keep their data locally and only | contribute their local collaborative information by

关键词： Recommender system | Federated learning | Privacy protection

摘要：, in this paper, a signed social recommender system | challenges in developing signed social recommender systems | Most approaches used in social recommender | systems concentrate mostly on positive relationships | within the social network, frequently ignoring the

关键词： Deep learning | Signed social recommender system | Network representation learning | Collaborative filtering

摘要： advancements in knowledge-enhanced Recommender Systems | Systems to propose a novel system, named AIREG for the | system with explicit factual knowledge from KGs. This | developments in knowledge-enhanced conversational Recommender | In the nowadays modern digital era, the

关键词： Recommender system | Large language model | Knowledge graph | Knowledge extraction

摘要：Recently, graph neural networks (GNNs) have | played a key crucial in many recommendation situations | . In particular, contrastive learning-based | hypergraph neural networks (HGNNs) are gradually becoming a | research focus for addressing issues of data sparsity and

关键词： Recommender system | Graph neural network | Hypergraph learning | Contrastive learning | Hypergraph structure

摘要： mechanisms into system* design, thereby enabling tourists to* | results suggest that our system* outperforms benchmarks* | system is also applicable for other predictable | © 2024 Elsevier B.V.Recommender systems have | been used extensively in tourism activities to aid

关键词： Crowding avoidance | Forecasting mechanism | Personalized tourist experience | Recommender system | Tourist trip design problem

摘要：Sequential recommender* systems, as a* | specialized branch of recommender* systems that can capture* | Sequential Recommender* Systems (FedSeqRecs), in which a* | public sequential recommender* model is shared and* | system users alike. Furthermore, since PTF-FSR only

关键词： Sequential Recommendation | Federated Learning | Contrastive Learning | Model Intellectual Property

关键词： Machine learning | Visualizations | Trust | Decision-Making

摘要： eating. However, if recommender* systems show them this* | intersection of recommender* systems, ED content, and users in* | As users social media feeds have become | increasingly driven by algorithmically recommended content | , there is a need to understand the impact these

关键词： Recommender systems | personalization | targeted advertising | eating disorders

摘要：Recommender systems are widely used in various | applications. Knowledge graphs are increasingly used to | improve recommendation performance by extracting | valuable information from user-item interactions. However | , current methods do not effectively use fine-grained

关键词： User preference | Knowledge graph | Recommender systems | Graph neural networks | Convolutional neural networks

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Recommender System Based on Diffusion Augmentation and | Sequential recommender systems often struggle | recommender dataset and use a diffusion augmentation | sequential recommender model based on their challenge | with accurate personalized recommendations due to

关键词： Diffusion Model | Data Augmentation | Federated Recommender System | Guided Denoising

摘要：Federated recommender systems (FedRecs) have | emerged as a popular research direction for protecting | users' privacy in on-device recommendations. In | FedRecs, users keep their data locally and only | contribute their local collaborative information by

关键词： Recommender system | Federated learning | Privacy protection

摘要：, in this paper, a signed social recommender system | challenges in developing signed social recommender systems | Most approaches used in social recommender | systems concentrate mostly on positive relationships | within the social network, frequently ignoring the

关键词： Deep learning | Signed social recommender system | Network representation learning | Collaborative filtering

摘要： advancements in knowledge-enhanced Recommender Systems | Systems to propose a novel system, named AIREG for the | system with explicit factual knowledge from KGs. This | developments in knowledge-enhanced conversational Recommender | In the nowadays modern digital era, the

关键词： Recommender system | Large language model | Knowledge graph | Knowledge extraction

摘要：Recently, graph neural networks (GNNs) have | played a key crucial in many recommendation situations | . In particular, contrastive learning-based | hypergraph neural networks (HGNNs) are gradually becoming a | research focus for addressing issues of data sparsity and

关键词： Recommender system | Graph neural network | Hypergraph learning | Contrastive learning | Hypergraph structure

摘要： mechanisms into system design, thereby enabling tourists to | results suggest that our system outperforms benchmarks | system is also applicable for other predictable | © 2024 Elsevier B.V.Recommender systems have | been used extensively in tourism activities to aid

关键词： Crowding avoidance | Forecasting mechanism | Personalized tourist experience | Recommender system | Tourist trip design problem

摘要：Sequential recommender systems, as a | specialized branch of recommender systems that can capture | Sequential Recommender Systems (FedSeqRecs), in which a | public sequential recommender model is shared and | system users alike. Furthermore, since PTF-FSR only

关键词： Sequential Recommendation | Federated Learning | Contrastive Learning | Model Intellectual Property

摘要： recommender system decisions. Our first study focuses on | recommender system to use. The second study focuses on low | The accuracy of recommender systems influences | decisions with recommender systems is under-explored. To | , validating decisions, and building trust in recommendation

关键词： Machine learning | Visualizations | Trust | Decision-Making

摘要： eating. However, if recommender systems show them this | intersection of recommender systems, ED content, and users in | As users social media feeds have become | increasingly driven by algorithmically recommended content | , there is a need to understand the impact these

关键词： Recommender systems | personalization | targeted advertising | eating disorders

摘要：Recommender systems are widely used in various | applications. Knowledge graphs are increasingly used to | improve recommendation performance by extracting | valuable information from user-item interactions. However | , current methods do not effectively use fine-grained

关键词： User preference | Knowledge graph | Recommender systems | Graph neural networks | Convolutional neural networks

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： Recommender System Based on Diffusion Augmentation and | Sequential recommender* systems often struggle* | recommender dataset and use a diffusion augmentation | sequential recommender* model based on their challenge* | with accurate personalized recommendations due to

摘要：Federated recommender* systems (FedRecs) have* | emerged as a popular research direction for protecting | users' privacy in on-device recommendations. In | FedRecs, users keep their data locally and only | contribute their local collaborative information by

摘要： mechanisms into system* design, thereby enabling tourists to* | results suggest that our system* outperforms benchmarks* | system is also applicable for other predictable | © 2024 Elsevier B.V.Recommender systems have | been used extensively in tourism activities to aid

摘要：Sequential recommender* systems, as a* | specialized branch of recommender* systems that can capture* | Sequential Recommender* Systems (FedSeqRecs), in which a* | public sequential recommender* model is shared and* | system users alike. Furthermore, since PTF-FSR only

摘要： eating. However, if recommender* systems show them this* | intersection of recommender* systems, ED content, and users in* | As users social media feeds have become | increasingly driven by algorithmically recommended content | , there is a need to understand the impact these