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Journal articles on the topic 'Dynamic user modeling'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yin, Hongzhi, Bin Cui, Ling Chen, Zhiting Hu, and Xiaofang Zhou. "Dynamic User Modeling in Social Media Systems." ACM Transactions on Information Systems 33, no. 3 (2015): 1–44. http://dx.doi.org/10.1145/2699670.

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Huang, Ko-Hsun, Yi-Shin Deng, and Ming-Chuen Chuang. "Static and Dynamic User Portraits." Advances in Human-Computer Interaction 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/123725.

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User modeling and profiling has been used to evaluate systems and predict user behaviors for a considerable time. Models and profiles are generally constructed based on studies of users’ behavior patterns, cognitive characteristics, or demographic data and provide an efficient way to present users’ preferences and interests. However, such modeling focuses on users’ interactions with a system and cannot support complicated social interaction, which is the emerging focus of serious games, educational hypermedia systems, experience, and service design. On the other hand, personas are used to port

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Ge, Qian, Daisuke Fukuda, Ke Han, and Wenjing Song. "Reservoir-based surrogate modeling of dynamic user equilibrium." Transportation Research Procedia 38 (2019): 772–91. http://dx.doi.org/10.1016/j.trpro.2019.05.040.

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Ge, Qian, Daisuke Fukuda, Ke Han, and Wenjing Song. "Reservoir-based surrogate modeling of dynamic user equilibrium." Transportation Research Part C: Emerging Technologies 113 (April 2020): 350–69. http://dx.doi.org/10.1016/j.trc.2019.10.010.

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Lu, Chung-Cheng, and Hani S. Mahmassani. "Modeling User Responses to Pricing." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (2008): 124–35. http://dx.doi.org/10.3141/2085-14.

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In a previous contribution, the authors showed how to incorporate user heterogeneity in determining equilibrium route choices in a network in response to pricing. Presented here is a generalization of that framework to incorporate joint consideration of route and departure time as well as heterogeneity in a wider range of behavioral characteristics. A multicriterion simultaneous route and departure time user equilibrium (MSRDUE) model is presented, along with a simulation-based algorithm intended for practical network applications. The model explicitly considers heterogeneous users with differ

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He, Tongze, Caili Guo, Yunfei Chu, Yang Yang, and Yanjun Wang. "Dynamic user modeling for expert recommendation in community question answering." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7281–92. http://dx.doi.org/10.3233/jifs-200729.

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Community Question Answering (CQA) websites has become an important channel for people to acquire knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, i.e., expert recommendation. However, a lot of existing methods consider the expert recommendation problem in a static context, ignoring that the real-world CQA websites are dynamic, with users’ interest and expertise changing over time. Although some methods that utilize time information have been proposed, their performance improvement can be limited due to fact that they fai

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Janarthanam, Srinivasan, and Oliver Lemon. "Adaptive Generation in Dialogue Systems Using Dynamic User Modeling." Computational Linguistics 40, no. 4 (2014): 883–920. http://dx.doi.org/10.1162/coli_a_00203.

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We address the problem of dynamically modeling and adapting to unknown users in resource-scarce domains in the context of interactive spoken dialogue systems. As an example, we show how a system can learn to choose referring expressions to refer to domain entities for users with different levels of domain expertise, and whose domain knowledge is initially unknown to the system. We approach this problem using a three step process: collecting data using a Wizard-of-Oz method, building simulated users, and learning to model and adapt to users using Reinforcement Learning techniques. We show that

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8

Ha, Taehyun, Bjorn Beijnon, Sangyeon Kim, Sangwon Lee, and Jang Hyun Kim. "Examining user perceptions of smartwatch through dynamic topic modeling." Telematics and Informatics 34, no. 7 (2017): 1262–73. http://dx.doi.org/10.1016/j.tele.2017.05.011.

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Wang, Lili, Chenghan Huang, Ying Lu, Weicheng Ma, Ruibo Liu, and Soroush Vosoughi. "Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks." ACM Transactions on Information Systems 40, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3472955.

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Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynami

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Kim, Sung-Hwan, and Hwan-Gue Cho. "User–Topic Modeling for Online Community Analysis." Applied Sciences 10, no. 10 (2020): 3388. http://dx.doi.org/10.3390/app10103388.

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Analyzing user behavior in online spaces is an important task. This paper is dedicated to analyzing the online community in terms of topics. We present a user–topic model based on the latent Dirichlet allocation (LDA), as an application of topic modeling in a domain other than textual data. This model substitutes the concept of word occurrence in the original LDA method with user participation. The proposed method deals with many problems regarding topic modeling and user analysis, which include: inclusion of dynamic topics, visualization of user interaction networks, and event detection. We c

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11

Chung, Doo-Shik, Hyeon Jo, and Soung-Hie Kim. "Dynamic Pricing for User Created Contents : Computer Modeling and Simulation." Journal of the Korea Contents Association 12, no. 6 (2012): 56–67. http://dx.doi.org/10.5392/jkca.2012.12.06.056.

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12

Wang, Ruiqin, Zongda Wu, Jungang Lou, and Yunliang Jiang. "Attention-based dynamic user modeling and Deep Collaborative filtering recommendation." Expert Systems with Applications 188 (February 2022): 116036. http://dx.doi.org/10.1016/j.eswa.2021.116036.

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13

Kim, Eunhui, and Munchurl Kim. "Topic-tracking-based dynamic user modeling with TV recommendation applications." Applied Intelligence 44, no. 4 (2015): 771–92. http://dx.doi.org/10.1007/s10489-015-0720-8.

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Todo, Hiroyoshi, Xiliang Zhang, Zhongguo Zhang, and Yuki Todo. "Dynamic User Tourism Interest Modeling through Domain Information Integration: A Hierarchical Approach." Electronics 13, no. 15 (2024): 2970. http://dx.doi.org/10.3390/electronics13152970.

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With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to construc

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15

Rowe, Jonathan, and James Lester. "Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 6, no. 1 (2010): 57–62. http://dx.doi.org/10.1609/aiide.v6i1.12403.

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Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian net

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Hassan, Md Rakib, Gour Karmakar, and Joarder Kamruzzaman. "Reputation and User Requirement Based Price Modeling for Dynamic Spectrum Access." IEEE Transactions on Mobile Computing 13, no. 9 (2014): 2128–40. http://dx.doi.org/10.1109/tmc.2013.78.

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Wei, Yun, Qing Shao, and Yin Han. "Modeling of Dynamic Game-Based Route Guidance System." Applied Mechanics and Materials 130-134 (October 2011): 1867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1867.

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Route guidance process is the dynamic game-based process of traffic authority and users, users’ behavior is affected by the guidance information and it also affect guidance effect. On the basis of analysis of user equilibrium and system optimum, a game theory model between traffic authority and users is presented, and system optimum and user optimum guidance strategy are given. Simulation results show that: user optimum guidance strategy will bring oscillation of traffic flow, system optimum guidance strategy is optimal but difficult to achieve, and coordination of system optimum and user opti

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Galimkair Mutanov, Sayabek Ziyadin, and Askhat Serikbekuly. "Application of System-Dynamic Modeling to Improve Distribution Logistics Processes in the Supply Chain." Communications - Scientific letters of the University of Zilina 22, no. 3 (2020): 29–39. http://dx.doi.org/10.26552/com.c.2020.3.29-39.

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This article presents an approach of improve logistics processes with the system dynamics modeling of two different processes scenarios. System dynamics is used, not only as a causal loop diagram, but calculated measures of end-user satisfaction indicators were provided by experts, as well. Literature review of fundamental definitions and recent ideas in subject of supply chain management (SCM), system dynamics (SD), business processes model and notation (BPMN) and distribution logistics for dozens of major sources, has been made. In the work, methods of expert interviews, content

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Lu, Chung-Cheng, and Hani S. Mahmassani. "Modeling heterogeneous network user route and departure time responses to dynamic pricing." Transportation Research Part C: Emerging Technologies 19, no. 2 (2011): 320–37. http://dx.doi.org/10.1016/j.trc.2010.05.017.

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Ingole, Deepak, Guilhem Mariotte, and Ludovic Leclercq. "Perimeter gating control and citywide dynamic user equilibrium: A macroscopic modeling framework." Transportation Research Part C: Emerging Technologies 111 (February 2020): 22–49. http://dx.doi.org/10.1016/j.trc.2019.11.016.

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21

Li, Zheng, Xueyuan Huang, Liupeng Gong, Ke Yuan, and Chun Liu. "Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation." ISPRS International Journal of Geo-Information 12, no. 9 (2023): 352. http://dx.doi.org/10.3390/ijgi12090352.

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Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users’ check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommenda

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22

Zhang, Lu, Shangxin Chang, Min Chen, Anqi Zhang, Hongming Li, and Sangbing Tsai. "Deep Learning-Based User Information Behavior Mining and Personalized Recommendation Optimization." Journal of Global Information Management 33, no. 1 (2025): 1–34. https://doi.org/10.4018/jgim.372058.

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Personalized recommendation systems have become crucial for enhancing user experience and driving engagement in various online platforms. However, existing methods face challenges in accurately modeling high-order user-item relationships, dynamically capturing user preferences, and effectively utilizing multi-modal data. These limitations hinder their ability to deliver relevant, diverse, and context-aware recommendations. To address these challenges, we propose the Graph Attention-based Dynamic Recommendation Framework (GADR). GADR incorporates a graph attention mechanism to prioritize high-o

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23

Cho, Junsu, Dongmin Hyun, Dong won Lim, Hyeon jae Cheon, Hyoung-iel Park, and Hwanjo Yu. "Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4199–207. http://dx.doi.org/10.1609/aaai.v37i4.25537.

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Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs focus on a single type of user behavior, only a few pay attention to multi-behavior sequences, although they are very common in real-world scenarios. It is challenging to effectively capture the user interests within multi-behavior sequences, because the information about user interests is entangled throughout the sequences in complex relationships. To this end, we first address the characteristics of multi-behavior seq

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Wang, Chun Ping. "Design and Implementation of Network Events Monitoring System." Applied Mechanics and Materials 568-570 (June 2014): 1430–33. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1430.

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The model of network monitoring system proposed in this paper, the use of user modeling techniques and event detection techniques. Preclude the use of dynamic modeling and dynamic model of the method of combining inferred more detailed user interest model to optimize the results, event detection method for the introduction of natural language processing, the system automatically send the text to identify hot topics and events advertising. In the system design, considering the intersection of the two, to obtain a better user experience.

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Zhang, Yanmei, Tingpei Lei, and Zhiguang Qin. "A Service Recommendation Algorithm Based on Modeling of Dynamic and Diverse Demands." International Journal of Web Services Research 15, no. 1 (2018): 47–70. http://dx.doi.org/10.4018/ijwsr.2018010103.

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This article contends that current service recommendation algorithms are still unable to meet the dynamic and diverse demands of users, so a service recommendation algorithm considering dynamic and diverse demands is proposed. The latent Dirichlet allocation model of machine learning field is adopted to extract the user implicit demand factors, and then the bipartite graph modeling and random-walk algorithm are used to extend implicit demand factors to predict short-term changes and diversity of user demand. At last, the service recommendation list is generated based on these demand factors. E

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COSTAGLIOLA, G., R. FRANCESE, A. MUSTO, and G. POLESE. "A METHODOLOGY FOR MODELING MULTIMEDIA DATABASES." International Journal of Software Engineering and Knowledge Engineering 12, no. 05 (2002): 523–43. http://dx.doi.org/10.1142/s0218194002001025.

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The modeling of multimedia databases in the context of multimedia information systems is a complex task. The designer has to model the structure and the dynamic behavior of multimedia objects, together with possible user interactions with them. These can include content-based queries, and dynamic presentations, which might both require the construction of special index structures on the stored data. In this paper we present a methodology for the design of multimedia databases. The methodology extends some of the Unified Modeling Language (UML) diagrams with constructs to model the structure of

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Wang, Xuan, Bin Wu, and Tong Wu. "LOMDP: Maximizing Desired Opinions in Social Networks by Considering User Expression Intentions." Entropy 27, no. 4 (2025): 360. https://doi.org/10.3390/e27040360.

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To address the problem of maximizing desired opinions in social networks, we present the Limited Opinion Maximization with Dynamic Propagation Optimization framework, which is grounded in information entropy theory. Innovatively, we introduce the concept of node expression capacity, which quantifies the uncertainty of users’ expression intentions via entropy and effectively identifies the impact of silent nodes on the propagation process. Based on this, in terms of seed node selection, we develop the Limited Opinion Maximization algorithm for multi-stage seed selection, which dynamically optim

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28

HUANG, Dan, Yi ZHENG, and Chang-jia CHEN. "Dynamic modeling approach to analyze user churn in a large P2P downloading system." Journal of China Universities of Posts and Telecommunications 20, no. 1 (2013): 19–25. http://dx.doi.org/10.1016/s1005-8885(13)60003-5.

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Yang, Hai, and Qiang Meng. "Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium." Transportation Research Part A: Policy and Practice 35, no. 10 (2001): 895–912. http://dx.doi.org/10.1016/s0965-8564(00)00030-6.

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30

Liu, Fengyu, Jinhe Chen, Jun Yu, and Rui Zhong. "Next Point of Interest (POI) Recommendation System Driven by User Probabilistic Preferences and Temporal Regularities." Mathematics 13, no. 8 (2025): 1232. https://doi.org/10.3390/math13081232.

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The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal m

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Yang, Xuelian, Jin Bai, and Xiaolin Wang. "Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU." Discrete Dynamics in Nature and Society 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/5666405.

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With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market

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Zhang, Yong Wang, Qing Liang Meng, Feng Pan, and Wei Zhao. "Modeling of Traction Power Supply System Based on the User-Defined Model of PSASP." Advanced Materials Research 1008-1009 (August 2014): 437–40. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.437.

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Using the user-defined model (UDM) provided by the power system analysis software package ( PSASP), This paper constructs traction power supply system model. The correctness of interface between user-defined model and PSASP is verified by independent dynamic characteristic program. It offers a new way of available practical model applying to power system simulation and research on the influence of traction loads to power system.

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Sun, Qinghao, Ziyang Li, Jiong Yu, Xue Li, and Xin Wang. "NRDPA: Review-Aware Neural Recommendation with Dynamic Personalized Attention." Electronics 14, no. 1 (2024): 33. https://doi.org/10.3390/electronics14010033.

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Review-based recommendation can utilize user and item features extracted from review text to alleviate the problems of data sparsity and poor interpretability. However, most existing methods focus on static modeling of user personality and item attributes while ignoring the dynamic changes of user and item features. Therefore, this paper proposes a neural recommendation method with dynamic personalized attention (NRDPA). First, this method captures the changes in user behavior at the word level and review level and models the personalized features of users and items by dynamically highlighting

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Yang, Hui, and Changchun Yang. "TIGNN-RL: Enabling time-sensitive and context-aware intelligent decision-making with dynamic graphs in recommender systems and biomechanics knowledge." Molecular & Cellular Biomechanics 22, no. 3 (2025): 1339. https://doi.org/10.62617/mcb1339.

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Intelligent decision-making in dynamic recommender systems is crucial for capturing temporal user preferences and optimizing long-term user satisfaction. Traditional recommender systems often rely on static modeling, neglecting the temporal dynamics of user-item interactions. To address this limitation, we propose a novel framework, Temporal Interpretability Graph Neural Network with Reinforcement Learning (TIGNN-RL), which integrates dynamic graph neural networks (DGNNs) and Proximal Policy Optimization (PPO) to optimize personalized recommendations. Specifically, our method models user-item

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Researcher. "INTELLIGENT INTERFACE ADAPTATION: MACHINE LEARNING APPROACH TO DYNAMIC MOBILE UI/UX." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 1279–90. https://doi.org/10.5281/zenodo.14400376.

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This article presents a comprehensive framework for implementing intelligent interface adaptation in mobile applications using machine learning approaches. The article addresses the critical challenges faced in modern mobile interface design, proposing innovative solutions for dynamic UI/UX optimization. Through the implementation of sophisticated behavioral modeling systems and advanced machine learning algorithms, the article demonstrates significant improvements in user engagement, task completion efficiency, and overall satisfaction. The framework incorporates real-time data processing, pr

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Fedorov, A. М., I. O. Datyev, and I. G. Vishnyakov. "Designing an information system for integrated topic analysis of social media big data." Ontology of Designing 14, no. 1 (2024): 55–70. http://dx.doi.org/10.18287/2223-9537-2024-14-1-55-70.

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Open communities of users in social media are a source of data that quickly presents the thematic agenda of issues relevant to the population. The indicators of user activity are views, likes, comments and reposts, and they are of a dynamic nature. The article presents a new vision at the topic modeling problems, the results of which are examined for dynamic properties. These data are relevant to solve problems of information support for regional and municipal de-velopment. The authors reveal their experience in designing an information system for integrated topic analysis of large open social

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Zhao, Qing, Shuguo Pan, Wang Gao, Xianlu Tao, Hao Liu, and Zeyu Zhang. "Crowdsourcing User-Enhanced PPP-RTK with Weighted Ionospheric Modeling." Remote Sensing 17, no. 6 (2025): 1099. https://doi.org/10.3390/rs17061099.

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In the conventional PPP-RTK mode, the platform and users act only as the generator and the utilizer of ionospheric corrections, respectively. In sparse reference station networks or regions with an active ionosphere, high-precision modeling still faces challenges. This study utilizes the concept of crowdsourcing and treats users as dynamic reference stations. By continuously feeding back ionospheric information to the platform, high-spatial-resolution modeling is achieved. Additionally, weight factors related to user positions are incorporated into conventional polynomial models to transform t

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Batzer, Stephen A., Alexander M. Gouskov, and Sergey A. Voronov. "Modeling Vibratory Drilling Dynamics." Journal of Vibration and Acoustics 123, no. 4 (2001): 435–43. http://dx.doi.org/10.1115/1.1387024.

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The dynamic behavior of deep-hole vibratory drilling is analyzed. The mathematical model presented allows the determination of axial tool and workpiece displacements and cutting forces for significant dynamic system behavior such as the engagement and disengagement of the cutting tool into the workpiece material and tool breakthrough. Model parameters include the actual rigidity of the tool and workpiece holders, time-varying chip thickness, time lag for chip formation due to tool rotation and possible disengagement of drill cutting edges from the workpiece due to tool and/or workpiece axial v

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Vani, V., R. Pradeep Kumar, and Mohan S. "Predictive Modeling of User Interaction Patterns for 3D Mesh Streaming." International Journal of Information Technology and Web Engineering 7, no. 4 (2012): 1–19. http://dx.doi.org/10.4018/jitwe.2012100101.

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The complexity in 3D virtual environment over the web is growing rapidly every day. This 3D virtual environment comprises of set of structured static and dynamic scenes and each scene has multiple 3D objects/meshes. Therefore, the granular level in any 3D virtual environments is the object. In 3D virtual environment, it is required to give user interactions for every 3D object and at any point of time, it is enough if the system streams and brings in only the visible portion of the object from server to the client by utilizing the limited network bandwidth and the limited client memory space.

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Ban, Xuegang (Jeff), Jong-Shi Pang, Henry X. Liu, and Rui Ma. "Modeling and solving continuous-time instantaneous dynamic user equilibria: A differential complementarity systems approach." Transportation Research Part B: Methodological 46, no. 3 (2012): 389–408. http://dx.doi.org/10.1016/j.trb.2011.11.002.

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Kim, Nayoung, and Heesun Kim. "Dynamic Traffic Modeling based on User Count and Time of Day in Radio Communication." Journal of Korean Institute of Information Technology 23, no. 1 (2025): 109–15. https://doi.org/10.14801/jkiit.2025.23.1.109.

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Zhang, Hua, Hao Huang, and Cheng Peng. "A Novel User Behavior Modeling Scheme for Edge Devices with Dynamic Privacy Budget Allocation." Electronics 14, no. 5 (2025): 954. https://doi.org/10.3390/electronics14050954.

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Federated learning (FL) enables privacy-preserving collaborative model training across edge devices without exposing raw user data, but it is vulnerable to privacy leakage through shared model updates, making differential privacy (DP) essential. Existing DP-based FL methods, such as fixed-noise DP, suffer from excessive noise injection and inefficient privacy budget allocation, which degrade model accuracy. To address these limitations, we propose an adaptive differential privacy mechanism that dynamically adjusts the noise based on gradient sensitivity, optimizing the privacy–accuracy trade-o

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in Nature and Society, Discrete Dynamics. "Retracted: Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU." Discrete Dynamics in Nature and Society 2023 (August 23, 2023): 1. http://dx.doi.org/10.1155/2023/9803560.

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Eshtiag, A. Abd Elrhman, and K.Salih Nadir. "Modeling Variation in SaaS Application." International Journal of Computer Science Issues 15, no. 3 (2018): 22–30. https://doi.org/10.5281/zenodo.1292404.

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Multi-tenancy gives SaaS application opportunity to realize economical goal for user and provider. For all centralized management there is an important thing to be handling in SaaS environment is the variation of user and system requirements. In this paper we used the feature model in the stage of domain analysis after selected extend that suitable for tenants, which presents the variability for dynamic properties exactly in configuring our model by used algorithm for selecting feature and account variability and commonality. We explained this features in variable homecare model and configured

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Liu, Jia Dong, Dong Mei Zhao, and Xu Zhang. "Modeling and Simulation of Grid-Connected Photovoltaic System." Applied Mechanics and Materials 48-49 (February 2011): 697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.697.

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Modeling and simulation of a grid-connected photovoltaic system (GCPS) is addressed in this paper to analyze its control performance and dynamics under changing irradiation. A circuit model of solar array considering irradiation is used to simulate its inherent characteristics and realized by a controlled current source. The controls of the GCPS and its electrical circuits are represented by user-defined and built-in components. The dynamic of the system in normal and fault conditions are dominated by its power controller and a method to set PI controller’s parameters is proposed in this paper

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Agarwal, Shikha. "Semantic Approach for Multifarious Ranked Novelty and Diversity in News Recommendations." Asian Journal of Engineering and Applied Technology 6, no. 2 (2017): 18–31. http://dx.doi.org/10.51983/ajeat-2017.6.2.821.

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Information overload on web creates lots of inconvenience for end users to unearth requisite information which instigate the demand of personalized recommendations. Recommender systems strive to attain just accuracy in recommendations based on the history of user preferences, resulting in over specialized recommendations. This leads to gradual loss of user’s interest in the system. These approaches also fail to recommend other products in long list which could be of user interest but user is not aware of. This in-turn leads to sparse user profiles.In dynamic domains, novelty and diversity in c

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Mashayekh, Alireza, Timothy Jacobs, Mark Patterson, and John Etcheverry. "Prediction of air–fuel ratio control of a large-bore natural gas engine using computational fluid dynamic modeling of reed valve dynamics." International Journal of Engine Research 18, no. 9 (2017): 900–908. http://dx.doi.org/10.1177/1468087416686224.

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Air–fuel ratio control of large-bore, two-stroke, natural gas engines, typically used in the oil and gas field, is critically important to maintain stable operation and emission compliance. Many two-stroke applications rely on reed valves to control air and gas induction, which can involve complicated gas flow behavior; standard gas dynamic relationships are typically insufficient to characterize such behavior. Computational fluid dynamic simulations offer the needed complexity, but even so the computational fluid dynamic models, as shown in this work, must also capture the dynamic behavior of

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Mason, Lee, Amy Berrington de Gonzalez, Montserrat Garcia-Closas, Stephen J. Chanock, Blànaid Hicks, and Jonas S. Almeida. "Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization." PLOS ONE 18, no. 4 (2023): e0277149. http://dx.doi.org/10.1371/journal.pone.0277149.

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Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic fore

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Wang, Jun. "User Behavior Analysis and Security Strategy Optimization of College English Learning Platform Using Dynamic Graph Convolutional Network." Network Security 2024, no. 9 (2025): 42–53. https://doi.org/10.70985/qnwmvn34.

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This paper aimed to address the shortcomings of current college English learning platforms in user behavior analysis and security protection, especially the lack of accurate abnormal behavior detection and dynamic identity authentication measures. By adopting DGCN (Dynamic Graph Convolutional Network) and XGBoost (eXtreme Gradient Boosting) classification models for abnormal behavior detection, a dynamic identity authentication optimization strategy based on behavior risk scoring is proposed to improve the security of the platform. The user embedding features output from DGCN are input into XG

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Tang, Hengyao, Qingdong Wang, and Guosong Jiang. "Discrete Dynamic Modeling Analysis Based on English Learning Motivation." Mathematical Problems in Engineering 2022 (August 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/6995411.

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With the popularization of the Internet, various online learning platforms have developed rapidly, providing users with abundant learning resources, and realizing personalized resource recommendation has become the development trend of online learning platforms. In this paper, a personalized learning recommendation model based on improved collaborative filtering is proposed. Firstly, a multilayer interest model of learners is established to accurately describe learners’ interest in knowledge topics, courses, and knowledge areas; then, in view of the sparse scoring matrix and cold-start problem

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