Relevant bibliographies by topics / Personalized Content-Delivery and ContextAware Content-Delivery Framework

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Academic literature on the topic 'Personalized Content-Delivery and ContextAware Content-Delivery Framework'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Personalized Content-Delivery and ContextAware Content-Delivery Framework"

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Alaa, Edein M. Qoussini. "Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 31–40. https://doi.org/10.35940/ijrte.D7832.13060325.

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<strong>Abstract:</strong> Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key personalization aspects: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims
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Shivakumar, Shailesh, and Venkata Suresh Pachigolla. "MCWDF." International Journal of Information Technology and Web Engineering 13, no. 1 (2018): 1–19. http://dx.doi.org/10.4018/ijitwe.2018010101.

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Segregating the web page content into logical chunks is one of the popular techniques for modular organization of web page. While chunk-based approach works well for public web scenarios, in case of mobile-first personalization cases, chunking strategy would not be as effective for performance optimization due to dynamic nature of the Web content and due to the nature of content granularity. In this paper, the authors propose a novel framework Micro chunk based Web Delivery Framework which proposes and uses a novel concept of “micro chunk”. The micro chunk based Web Delivery framework aims to
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Qoussini, Alaa Edein M., and Shaima M. Al-Tabib. "Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 31–40. https://doi.org/10.35940/ijrte.d7832.13060325.

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Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key personalization aspects: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims to enhance student performa
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Hu, Jing, Yaling Lai, Ao Peng, Xuemin Hong, and Jianghong Shi. "Proactive Content Delivery with Service-Tier Awareness and User Demand Prediction." Electronics 8, no. 1 (2019): 50. http://dx.doi.org/10.3390/electronics8010050.

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Cost-effective delivery of massive data content is a pressing challenge facing modern mobile communication networks. In the literature, two primary approaches to tackle this challenge are service-tier differentiation and personalized proactive content caching. However, these two approaches have not been integrated and studied in a unified framework. This paper proposes an integrated proactive content delivery scheme that jointly exploits the availability of multiple service tiers and multi-user behavior prediction. Three optimal algorithms and one heuristic algorithm are introduced to solve th
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Nadila, Dewi, Mezan El-Khaeri Kesuma, Mala Pratiiwi, Yurika Witazora, and Khairun Nita Aulia. "The Impact of Artificial Intelligence on User Engagement and Content Delivery in Digital Media." KnE Social Sciences 10, no. 14 (2025): 620–35. https://doi.org/10.18502/kss.v10i14.19126.

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The advent of artificial intelligence (AI) is transforming the landscape of digital media platforms, offering unprecedented opportunities for enhancing user engagement and optimizing content delivery. This research aims to explore the transformative potential of AI in enhancing digital media platforms by creating more personalized and engaging content experiences for users. The research methodology is based on a comprehensive literature review, encompassing research aims and boundaries, search approaches, surveys, and the impact on the research framework. The article examines case studies of l
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Luo, Zhengrong, Jinyang Jiang, Huajie Zhou, and Jian Wu. "Enhancing Personalized Services in Tianfu Cultural Tourism: Pathways and Strategies Based on User Profiling." Philosophy and Social Science 1, no. 9 (2024): 86–93. https://doi.org/10.62381/p243915.

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Against the backdrop of a burgeoning global cultural tourism industry, Tianfu cultural tourism has emerged as a vital driver of economic growth in Sichuan, owing to its unique natural and cultural resources. To address the growing demand for personalized experiences among tourists, this study explores the application of user profiling technologies to enhance the delivery of personalized services in Tianfu cultural tourism. Leveraging big data and artificial intelligence, the study proposes a service optimization framework centered on data collection, profile analysis, and precise content deliv
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Danish, Fajer, Naeem Akhtar, and Muhammad Imran. "AI-Driven Personalization in Educational Marketing: A Framework for Enhancing Student Recruitment and Retention." Journal of Political Stability Archive 3, no. 2 (2025): 559–90. https://doi.org/10.63468/jpsa.3.2.33.

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Higher education universities require marketing knowledge to overcome growing student enrollment and retention issues in universities. This research examines how educational institutions perform in commercialization alongside studying AI customization effects on that area. In the study narrative literature review combined with secondary data analysis enabled researchers to create a conceptual framework which demonstrates how artificial intelligence tools should be applied for awareness, engagement, conversion, retention, and Advocacy phases. The study investigates three technological solutions
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Jin, Jennifer, and Mira Kim. "GPT-Empowered Personalized eLearning System for Programming Languages." Applied Sciences 13, no. 23 (2023): 12773. http://dx.doi.org/10.3390/app132312773.

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The eLearning approach to programming language instruction has gained widespread acceptance due to advantages such as accessibility, temporal flexibility, and content reusability. However, the current eLearning for programming predominantly employs the delivery of one-size-fits-all content, engendering elevated costs in both the development of language coursework and administration of eLearning sessions, which includes the labor-intensive task of grading student submissions. A compelling research question to consider is how to construct an eLearning system capable of delivering personalized, s
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Gonul, Suat, Tuncay Namli, Sasja Huisman, Gokce Banu Laleci Erturkmen, Ismail Hakki Toroslu, and Ahmet Cosar. "An expandable approach for design and personalization of digital, just-in-time adaptive interventions." Journal of the American Medical Informatics Association 26, no. 3 (2018): 198–210. http://dx.doi.org/10.1093/jamia/ocy160.

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AbstractObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. T
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Surjadeep Dutta. "Leveraging AI Chatbots for Personalized Content Delivery: An Empirical Study in the Pharmaceutical Industry." Journal of Intelligent Communication 4, no. 1 (2025): 98–113. https://doi.org/10.54963/jic.v4i1.1265.

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This empirical study examines the strategic incorporation of AI-driven chatbots in the pharmaceutical sector for tailored content distribution, improved user engagement, and data-driven decision-making. The digital revolution is reshaping pharmaceutical marketing, with AI chatbots providing a scalable, round-the-clock solution for engaging patients, healthcare professionals, and consumers while ensuring compliance within a regulated framework. A quantitative methodology was employed to gather data from 120 participants within the Indian pharmaceutical industry. Findings from reliability analys
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Books on the topic "Personalized Content-Delivery and ContextAware Content-Delivery Framework"

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Lennon, Sharron J., Kim K. P. Johnson, and Nancy A. Rudd. Social Psychology of Dress. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781501391316.

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Social Psychology of Dress, Second Edition presents and explains the major theories and concepts of human behavior relating to dress, drawing from the social science fields of psychology, sociology, and anthropology. The text positions dress as a process in which individual preferences, membership in social groups, and cultural awareness all impact choices about attire and appearance. Using empirical data and examples from current events and popular culture, the authors define dress, present its origins and functions, and discuss research methods for dress. They also explore the relationships
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Book chapters on the topic "Personalized Content-Delivery and ContextAware Content-Delivery Framework"

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Tsatsou, Dorothea, Symeon Papadopoulos, Ioannis Kompatsiaris, and Paul C. Davis. "Distributed Technologies for Personalized Advertisement Delivery." In Advances in Multimedia and Interactive Technologies. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-189-8.ch013.

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This chapter provides an overview on personalized advertisement delivery paradigms on the web with a focus on the recommendation of advertisements expressed in or accompanied by text. Different methods of online targeted advertising will be examined, while justifying the need for channeling the appropriate ads to the corresponding users. The aim of the work presented here is to illustrate how the semantic representation of ads and user preferences can achieve optimal and unobtrusive ad delivery. We propose a set of distributed technologies that efficiently handles the lack of textual data in a
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Hafeez, Yasir, Syed Hasan Adil, Mansoor Ebrahim, and Mirzan Izzfitri Bin Mahadir. "Speech Emotion Recognition-Based Music Recommender." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9057-3.ch008.

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The objective of this chapter is to provide details implementation of a research project; song recommendations based on speech emotions through Speech Emotion Recognition (SER). This involves developing a Speech Emotion Recognition model utilizing neural network algorithms or deep learning techniques. The selected algorithms include a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, a Dense Neural Network (DNN), and a custom hybrid algorithm combining CNN and LSTM. A PyQT5 application framework was implemented to facilitate song recommendations. Users can record the
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Amala, K. J., D. Rajeswari, and Athish Venkatachalam. "Hybrid HMM-XGBoost Model for Smart Adaptive Learning Platforms." In Harnessing E-Learning to Create a Sustainable Future. IGI Global Scientific Publishing, 2025. https://doi.org/10.4018/979-8-3373-0112-9.ch009.

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Integrating machine learning models into e-learning greatly improves the flexibility and personalization of learning environments. In this study, combine XGBoost with Hidden Markov Model (HMM) as a new way to improve the design of adaptive e-learning systems. HMM models students' changing learning states by looking at patterns in their learning over time. XGBoost, a well-known gradient boosting framework known for its accuracy and effectiveness, is used to predict how well students would do based on data from the past and create personalized learning paths. The proposed hybrid model takes the
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Wang, Weisong, and Mingzhu Wei. "Research on the Application of Big Data in Ideological and Political Education in Guangxi Universities in the New Era." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250463.

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The emergence of the wisdom teaching model addresses the triple imperatives of social progress, educational evolution, and holistic human development. This innovative approach leverages smart technologies to create adaptive learning ecosystems that prioritize learner autonomy and value internalization. Our research developed an APH-based evaluation framework for ideological and political education (IPE), enabling systematic assessment of multi-dimensional learning outcomes. Complementing this, we built an integrated online platform featuring personalized content delivery, interactive simulatio
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Alias, Nurul Fitriah. "Pedagogical Practices of Microlearning Educational Experience." In Advances in Educational Technologies and Instructional Design. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8452-7.ch007.

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This chapter explores the development of microlearning pedagogical practices aimed at enhancing student learning in higher education through a design-based research (DBR) approach. Recognizing microlearning's potential for quick, focused learning, the study addresses the lack of strong pedagogical foundations and practical implementation challenges. The research progresses through four phases: analyzing current teaching practices, designing key principles, developing pedagogical strategies, and evaluating their usability via expert feedback. Using surveys and applied cognitive task analysis (A
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Georgiou, Stalo. "Applying Strategic Models in Distance Education and Educational Communication." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-5583-1.ch014.

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In the rapidly evolving landscape of education, distance learning has emerged as a pivotal modality, necessitating the strategic application of educational frameworks to ensure effectiveness, engagement, and communication. This article explores the integration of strategic models within distance education and educational communication to optimize learning outcomes. Among these models, the Community of Inquiry (CoI) framework emphasizes the importance of cognitive presence, social presence, and teaching presence in creating an interactive and meaningful learning experience. Additionally, the AD
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Subrahmanyam, Satya. "Developing Digital Citizenship Skills." In Advances in Computational Intelligence and Robotics. IGI Global Scientific Publishing, 2025. https://doi.org/10.4018/979-8-3373-1062-6.ch012.

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In the era of Society 5.0, where technology increasingly shapes society, developing digital citizenship skills has become essential. This chapter explores the integration of artificial intelligence (AI) into curriculum innovation to foster digital citizenship, emphasizing the need for ethical, responsible, and effective participation in the digital realm. It defines digital citizenship as a blend of technical proficiency and ethical responsibility, discussing AI's role in personalized learning and content delivery. The chapter presents a framework for developing core competencies like digital
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Conference papers on the topic "Personalized Content-Delivery and ContextAware Content-Delivery Framework"

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Howlin, Com, and Danny Lynch. "A framework for the delivery of personalized adaptive content." In 2014 International Conference on Web & Open Access to Learning (ICWOAL). IEEE, 2014. http://dx.doi.org/10.1109/icwoal.2014.7009203.

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Eldridge, Bryan, and Cameron Stevens. "APPLYING A GENERATIONAL-ORIENTED AND FLEXIBLE ASSESSMENT AND EVALUATION FRAMEWORK TO DELIVER HIGHLY PERSONALIZED AND EFFICIENT LEARNING ACTIVITIES." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-164.

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For the first time in workplace history, there are five generations are in the workplace simultaneously: the Traditionalists (born between 1925 and 1945), the Baby Boomers (born between 1946 and 1964), Generation X (born between 1965 and 1980), Generation Y (born between 1981 and 1994), and the Millennials (born after 1995). Accommodating the unique learning and development needs of such a diverse spectrum of learners in the organizational enterprise have provided significant procedural and cultural roadblocks for facilitating efficient learning content delivery and personalization. A potentia
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