This article evaluates the evolution of inter-city R&D technology spillover in the Yangtze River Delta Region (YRDR) using social network analysis method. Empirical results indicate that the inter-city R&D tec...
This article evaluates the evolution of inter-city R&D technology spillover in the Yangtze River Delta Region (YRDR) using social network analysis method. Empirical results indicate that the inter-city R&D technology spillovers are sparse, but gradually increasing. Four characteristic cohesive subgroups are formed, subgroup I formed by core cities like Shanghai and Nanjing is the technical sender and the subgroup is closely connected. Nanjing's role as a network bridge has been replaced by Jiaxing.
Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance understanding of the disorder mechanisms and early intervention. However, due to the small sample size...
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作者：
Xie, YuanZou, TaoYang, JunjieSun, WeijunXie, ShengliSchool of Mechanical and Electrical Engineering
Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation
Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation
Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation
Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China
Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverbera...
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The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and soc...
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Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the divers...
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Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the diverse nature of social relationships. Some individuals with independent personalities often prioritize their own interests over friends’ advice when making purchase decisions. Conversely, those who seek advice from others are more susceptible to social influence. Moreover, existing methods tend to overlook redundant and noisy social relationships within the network, hindering their ability to achieve accurate recommendations. In response, this paper proposes a novel counterfactual method to understand the causal factors driving purchase behaviors, thereby identifying the influence of users’ friends on their purchase decisions. By answering counterfactual questions about the influence of a friend's purchase behavior on the user's choices, we develop a causal model to represent social influence in the network. Our proposed refinement strategy, grounded in causal inference, generates counterfactual purchase behavior and guides the refinement of the social graph. Moreover, we present tailored graph refinement methods at various levels, ensuring fine-grained improvements. Experimental results on benchmark data demonstrate that the application of our strategy to different social recommendation models significantly enhances their predictive performance. The source code has been made available on https://***/LDY911/CFRSSR-Code.
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences f...
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Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decisionprocess of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN.
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