NSTL国家科技图书文献中心

1. KEMB-Rec: Knowledge-Enhanced Explainable Multibehavior Recommendation With Graph Contrastive Learning NSTL国家科技图书文献中心

Feipeng Guo | Zifan Wang - 《IEEE internet of things journal》 - 2025,12(4) - 3563～3576 - 共14页

摘要： recommendation systems overlook the diversity of user behaviors | multityped user* behaviors to mine the heterogeneous* | and rely solely on utilizing a singular form of user | behaviors, learning of users personalized behavioral | learning, comprising two modules. The first is the user

关键词： Contrastive learning | Semantics | Task analysis | Internet of Things | Data mining | Recommender systems | Accuracy

2. Augmented Motion Representation Learning Based on Virtual Reality Sports Game Review Data NSTL国家科技图书文献中心

Jing Cao | Gang Zhao ... - 《HCI International 2024 - Late Breaking Papers,Part IV》 - International Conference on Human-Computer Interaction - 2025, - 296～310 - 共15页

摘要： neural networks: one learns user* behaviors from reviews* | . The analysis revealed significant user* behaviors and* | , performance analysis, and user* engagement. Despite numerous* | physical abilities, understanding user* experience (UX* | : To analyze user* experiences in VR sports games, we*

关键词： Virtual reality | Augmented learning | Embodied interaction | Game review | Motion representation

3. A Transformer and LSTM Model for Electricity Consumption Forecasting and User's Behavior Influence NSTL国家科技图书文献中心

Laldja Ziani | Anis Chawki Abbes ... - 《Web Information Systems Engineering - WISE 2024,Part IV》 - International Conference on Web Information Systems Engineering - 2025, - 349～363 - 共15页

摘要：. Influencing user* behaviors towards sustainable electricity* | leading to more effective user* behaviors influence and* | this issue, the Internet of Behaviors* (IoB) has* | Consumer behavior and habits play a crucial | role in household energy consumption patterns

关键词： Internet of behaviors | Electricity consumption forecasting | Transformer | LSTM

4. MHHCR: Multi-behavior Heterogeneous Hypergraph Contrastive Recommendation NSTL国家科技图书文献中心

Yiheng Li | Weihai Lu - 《Web Information Systems Engineering - WISE 2024,Part III》 - International Conference on Web Information Systems Engineering - 2025, - 91～102 - 共12页

摘要： can simultaneously consider various user behaviors | , comments, etc., thus more comprehensively capturing user | , large-scale auxiliary behaviors* contain a lot of noise* | Multi-behavior graph recommendation systems | on the platform, such as clicks, purchases

关键词： Multi-behavior recommendation | Contrastive learning | Graph neural networks

5. Counterfactual Debasing for Multi-behavior Recommendations NSTL国家科技图书文献中心

Sirui Huang | Qian Li ... - 《Database Systems for Advanced Applications,Part III》 - International Conference on Database Systems for Advanced Applications | International Workshop on Big Data Management and Service | International Workshop on Graph Data Management and Analysis | International Workshop on Big Data Quality Management | Workshop on Emerging Results inData Science and Engineering - 2025, - 164～179 - 共16页

摘要： user behaviors, MBR becomes more vulnerable to | enhance recommendation performance with multi-typed user | outcomes, given various user* behavioral data as* | recommender (C-MBR), which models user* preferences from* | Multi-behavior recommendations (MBRs) aim to

关键词： Multi-behavior modeling | Deconfounded recommendation | Counterfactual explanation

6. Causal Behavior Pattern Inference for News Recommendation Through Multi-interest Matching NSTL国家科技图书文献中心

Xingming Chen | Wenqi Fan ... - 《Web Information Systems Engineering - WISE 2024,Part III》 - International Conference on Web Information Systems Engineering - 2025, - 179～190 - 共12页

摘要： of causal patterns in user* behaviors. Unlike* | behaviors are generally driven by diverse and evolving | . Uncovering causal relationships among user* interests can* | that models user* behavior from a causal perspective* | changes in user* preferences. By mapping interest-level*

关键词： Personalized news recommendation | Interest matching | Causal discovery

7. The Role of Portrayals of Risky Behavior in Alcohol-Branded eWOM in Driving Online Engagement NSTL国家科技图书文献中心

London Jr, Jackie | Yeh, Marie A. - 《International Journal of Electronic Commerce》 - 2025,29(1) - 131～154 - 共24页

摘要： strategy: depictions of risky alcohol behaviors* (e.g* | risky behaviors* and their effects on exposure and* | behaviors are associated with increases in eWOM exposure | semantic analysis of comments shows that risky behaviors | risky alcohol behaviors* in eWOM and shows that while*

关键词： Alcohol abuse | risky behaviors | online engagement | content analysis | online videos | electronic word-of-mouth | eWOM | social media | user-generated content

8. A Derivative Topic Propagation Model Based on Multidimensional Cognition and Game Theory NSTL国家科技图书文献中心

Qian Li | Long Gao ... - 《IEEE transactions on cognitive and developmental systems》 - 2025,17(1) - 189～204 - 共16页

摘要： among users by considering user* behaviors as* | well as the complexity of user* cognition and the* | issue of user* emotional reactions in mining topics. In* | networks. Second, considering that user* behavior is* | sensibility and rationality in the user* decision-making*

关键词： Cognition | Social networking (online) | Game theory | Analytical models | Emotional responses | Sentiment analysis | Mathematical models

9. Feed4Cloud: Towards trustworthy QoE-aware cloud service monitoring using blockchain NSTL国家科技图书文献中心

Ioanna Angeliki Kape... | Christos-Alexandros ...... - 《Future generations computer systems》 - 2025,163(Feb.) - 107532.1～107532.16 - 共16页

摘要： wide range of system conditions and user* behaviors.* | users. In this paper, we posit that user* feedback* | trustworthiness of user* feedback should not be considered* | exclusively trustworthy user* feedback for QoE-aware cloud* | intermediate verification layer that validates user* feedback*

关键词： Cloud computing | Blockchain | Quality of Experience (QoE) | Reputation | User feedback

10. Intertwined Biases Across Social Media Spheres: Unpacking Correlations in Media Bias Dimensions NSTL国家科技图书文献中心

Yifan Liu | Yike Li ... - 《Social Networks Analysis and Mining,Part II》 - International Conference on Advances in Social Networks Analysis and Mining - 2025, - 107～116 - 共10页

摘要： dynamic nature of social media, with its shifting user | behaviors and trends, further challenges the efficacy of | Biased information on social media | significantly influences public perception by reinforcing | stereotypes and deepening societal divisions. Previous

摘要： recommendation systems overlook the diversity of user behaviors | multityped user* behaviors to mine the heterogeneous* | and rely solely on utilizing a singular form of user | behaviors, learning of users personalized behavioral | learning, comprising two modules. The first is the user

关键词： Contrastive learning | Semantics | Task analysis | Internet of Things | Data mining | Recommender systems | Accuracy

关键词： Virtual reality | Augmented learning | Embodied interaction | Game review | Motion representation

摘要：. Influencing user* behaviors towards sustainable electricity* | leading to more effective user* behaviors influence and* | this issue, the Internet of Behaviors* (IoB) has* | Consumer behavior and habits play a crucial | role in household energy consumption patterns

关键词： Internet of behaviors | Electricity consumption forecasting | Transformer | LSTM

摘要： can simultaneously consider various user behaviors | , comments, etc., thus more comprehensively capturing user | , large-scale auxiliary behaviors* contain a lot of noise* | Multi-behavior graph recommendation systems | on the platform, such as clicks, purchases

关键词： Multi-behavior recommendation | Contrastive learning | Graph neural networks

摘要： user behaviors, MBR becomes more vulnerable to | enhance recommendation performance with multi-typed user | outcomes, given various user* behavioral data as* | recommender (C-MBR), which models user* preferences from* | Multi-behavior recommendations (MBRs) aim to

关键词： Multi-behavior modeling | Deconfounded recommendation | Counterfactual explanation

摘要： of causal patterns in user* behaviors. Unlike* | behaviors are generally driven by diverse and evolving | . Uncovering causal relationships among user* interests can* | that models user* behavior from a causal perspective* | changes in user* preferences. By mapping interest-level*

关键词： Personalized news recommendation | Interest matching | Causal discovery

摘要： strategy: depictions of risky alcohol behaviors* (e.g* | risky behaviors* and their effects on exposure and* | behaviors are associated with increases in eWOM exposure | semantic analysis of comments shows that risky behaviors | risky alcohol behaviors* in eWOM and shows that while*

关键词： Alcohol abuse | risky behaviors | online engagement | content analysis | online videos | electronic word-of-mouth | eWOM | social media | user-generated content

摘要： among users by considering user* behaviors as* | well as the complexity of user* cognition and the* | issue of user* emotional reactions in mining topics. In* | networks. Second, considering that user* behavior is* | sensibility and rationality in the user* decision-making*

关键词： Cognition | Social networking (online) | Game theory | Analytical models | Emotional responses | Sentiment analysis | Mathematical models

关键词： Cloud computing | Blockchain | Quality of Experience (QoE) | Reputation | User feedback

摘要： dynamic nature of social media, with its shifting user | behaviors and trends, further challenges the efficacy of | Biased information on social media | significantly influences public perception by reinforcing | stereotypes and deepening societal divisions. Previous

关键词： Social media | Bias identification | Benchmark | Datasets

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： recommendation systems overlook the diversity of user behaviors | multityped user behaviors to mine the heterogeneous | and rely solely on utilizing a singular form of user | behaviors, learning of users personalized behavioral | learning, comprising two modules. The first is the user

关键词： Contrastive learning | Semantics | Task analysis | Internet of Things | Data mining | Recommender systems | Accuracy

摘要： neural networks: one learns user behaviors from reviews | . The analysis revealed significant user behaviors and | , performance analysis, and user engagement. Despite numerous | physical abilities, understanding user experience (UX | : To analyze user experiences in VR sports games, we

关键词： Virtual reality | Augmented learning | Embodied interaction | Game review | Motion representation

摘要：. Influencing user behaviors towards sustainable electricity | leading to more effective user behaviors influence and | this issue, the Internet of Behaviors (IoB) has | Consumer behavior and habits play a crucial | role in household energy consumption patterns

关键词： Internet of behaviors | Electricity consumption forecasting | Transformer | LSTM

摘要： can simultaneously consider various user behaviors | , comments, etc., thus more comprehensively capturing user | , large-scale auxiliary behaviors contain a lot of noise | Multi-behavior graph recommendation systems | on the platform, such as clicks, purchases

关键词： Multi-behavior recommendation | Contrastive learning | Graph neural networks

摘要： user behaviors, MBR becomes more vulnerable to | enhance recommendation performance with multi-typed user | outcomes, given various user behavioral data as | recommender (C-MBR), which models user preferences from | Multi-behavior recommendations (MBRs) aim to

关键词： Multi-behavior modeling | Deconfounded recommendation | Counterfactual explanation

摘要： of causal patterns in user behaviors. Unlike | behaviors are generally driven by diverse and evolving | . Uncovering causal relationships among user interests can | that models user behavior from a causal perspective | changes in user preferences. By mapping interest-level

关键词： Personalized news recommendation | Interest matching | Causal discovery

摘要： strategy: depictions of risky alcohol behaviors (e.g | risky behaviors and their effects on exposure and | behaviors are associated with increases in eWOM exposure | semantic analysis of comments shows that risky behaviors | risky alcohol behaviors in eWOM and shows that while

关键词： Alcohol abuse | risky behaviors | online engagement | content analysis | online videos | electronic word-of-mouth | eWOM | social media | user-generated content

摘要： among users by considering user behaviors as | well as the complexity of user cognition and the | issue of user emotional reactions in mining topics. In | networks. Second, considering that user behavior is | sensibility and rationality in the user decision-making

关键词： Cognition | Social networking (online) | Game theory | Analytical models | Emotional responses | Sentiment analysis | Mathematical models

摘要： wide range of system conditions and user behaviors. | users. In this paper, we posit that user feedback | trustworthiness of user feedback should not be considered | exclusively trustworthy user feedback for QoE-aware cloud | intermediate verification layer that validates user feedback

关键词： Cloud computing | Blockchain | Quality of Experience (QoE) | Reputation | User feedback

摘要： dynamic nature of social media, with its shifting user | behaviors and trends, further challenges the efficacy of | Biased information on social media | significantly influences public perception by reinforcing | stereotypes and deepening societal divisions. Previous

关键词： Social media | Bias identification | Benchmark | Datasets

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： recommendation systems overlook the diversity of user behaviors | multityped user* behaviors to mine the heterogeneous* | and rely solely on utilizing a singular form of user | behaviors, learning of users personalized behavioral | learning, comprising two modules. The first is the user

摘要：. Influencing user* behaviors towards sustainable electricity* | leading to more effective user* behaviors influence and* | this issue, the Internet of Behaviors* (IoB) has* | Consumer behavior and habits play a crucial | role in household energy consumption patterns

摘要： can simultaneously consider various user behaviors | , comments, etc., thus more comprehensively capturing user | , large-scale auxiliary behaviors* contain a lot of noise* | Multi-behavior graph recommendation systems | on the platform, such as clicks, purchases

摘要： user behaviors, MBR becomes more vulnerable to | enhance recommendation performance with multi-typed user | outcomes, given various user* behavioral data as* | recommender (C-MBR), which models user* preferences from* | Multi-behavior recommendations (MBRs) aim to

摘要： of causal patterns in user* behaviors. Unlike* | behaviors are generally driven by diverse and evolving | . Uncovering causal relationships among user* interests can* | that models user* behavior from a causal perspective* | changes in user* preferences. By mapping interest-level*

摘要： strategy: depictions of risky alcohol behaviors* (e.g* | risky behaviors* and their effects on exposure and* | behaviors are associated with increases in eWOM exposure | semantic analysis of comments shows that risky behaviors | risky alcohol behaviors* in eWOM and shows that while*

摘要： among users by considering user* behaviors as* | well as the complexity of user* cognition and the* | issue of user* emotional reactions in mining topics. In* | networks. Second, considering that user* behavior is* | sensibility and rationality in the user* decision-making*