Relevant bibliographies by topics / Intelligent recommendation system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent recommendation system'

Author: Grafiati
Published: 16 March 2023
Last updated: 30 July 2025

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Journal articles on the topic "Intelligent recommendation system"

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Kathait, ShailendraSingh, Shubhrita Tiwari, and PiyushKumar Singh. "INTELLIGENT RECOMMENDATION SYSTEM." International Journal of Advanced Research 5, no. 2 (2017): 1649–56. http://dx.doi.org/10.21474/ijar01/3328.

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R, Rajasekar, Niranchana Radhakrishnan, Sridar K, et al. "Intelligent movie recommendation system." Salud, Ciencia y Tecnología - Serie de Conferencias 4 (March 12, 2025): 1438. https://doi.org/10.56294/sctconf20251438.

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A smart piece of technology, a movie recommendation system uses intricate algorithms to deliver users personalized movie recommendations, enhancing their viewing experience. By utilizing many data sources, including user ratings, movie metadata, and viewing history, the system creates comprehensive user profiles that encompass distinct interests and behaviors. Content-based filtering, which considers storyline keywords, character, and genre, suggests movies that are similar to the ones the buyer has already enjoyed. Collaborative filtering approaches enhance suggestions even further by identif
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Mishra, Ikshita, Ankita Sharma, and Tanuj Deria. "Intelligent Tourist Recommendation System." IJARCCE 6, no. 4 (2017): 384–91. http://dx.doi.org/10.17148/ijarcce.2017.6474.

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K, Abhay Sankar. "Intelligent Travel Recommendation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3122–29. http://dx.doi.org/10.22214/ijraset.2023.54131.

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Abstract: This survey paper presents a project focused on developing an online platform designed to provide users with efficient recommendations to enhance their travel experiences. The platform aims to assist users in selecting their preferred location of stay, exploring recommended places to visit, choosing appropriate modes of transportation, and identifying suitable dining options, all while considering the user's budget. The project incorporates various filtering algorithms to ensure accurate and tailored recommendations. Through this platform, users can create personalized itineraries al
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Shiva, Derangula. "Personalized Book Intelligent Recommendation System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49744.

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Abstract - As libraries undergo digital transformation, accompanied by advancements in information technology in academic libraries, it is evident that readers will want different and personalized services in addition to checking out books. Along with meeting the changing expectations of users, this research proposes an enhanced item-based collaborative-filtering recommendation algorithm that employs an average model representation to improve the accuracy measurements, as well as the use of Neural Collaborative Filtering (NCF) for modeling the user-item interaction by utilizing deep neural net
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Rtili, Mohammed Kamal, Ali Dahmani, and Mohamed Khaldi. "Recommendation System Based on the Learners' Tracks in an Intelligent Tutoring System." Journal of Advances in Computer Networks 2, no. 1 (2014): 40–43. http://dx.doi.org/10.7763/jacn.2014.v2.79.

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Sheshnarayan Pandey, Komal. "“TripMitra : Travel Recommendation System”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45112.

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Tourism is a rapidly growing industry, and travelers often seek personalized recommendations to explore destinations that align with their interests. TripMitra is a web-based travel recommendation system designed to provide intelligent and customized travel suggestions based on user preferences and interactions. The system incorporates machine learning algorithms to enhance the accuracy of recommendations and features an image-based search that allows users to upload a photo to find visually similar destinations.Developed using Flask (Python) for backend processing, OpenCV for image analysis,
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Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

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Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same produc
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., Jay Borade. "INTELLIGENT AGENT FOR TOURISM RECOMMENDATION SYSTEM." International Journal of Research in Engineering and Technology 07, no. 04 (2018): 39–46. http://dx.doi.org/10.15623/ijret.2018.0704007.

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Dissertations / Theses on the topic "Intelligent recommendation system"

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Thiengburanathum, Pree. "An intelligent destination recommendation system for tourists." Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30571/.

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Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First,
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Xu, Shuting. "Study and Design of an Intelligent Preconditioner Recommendation System." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/327.

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There are many scientific applications in which there is a need to solve very large linear systems. The preconditioned Krylove subspace methods are considered the preferred methods in this field. The preconditioners employed in the preconditioned iterative solvers usually determine the overall convergence rate. However, choosing a good preconditioner for a specific sparse linear system arising from a particular application is the combination of art and science, and presents a formidable challenge for many design engineers and application scientists who do not have much knowledge of preconditio
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Zhang, Junjie. "Development of a consumer-oriented intelligent garment recommendation system." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10026/document.

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Maintenant, l’achat de vêtements sur l’Internet est devenu une tendance importante pour les consommateurs du monde entier. Pourtant, dans les différents systèmes de vente en ligne, il manque systématiquement de recommandations personnalisées, comme celles fournies par les vendeurs d’une boutique physique, afin de proposer les produits les mieux adaptés à des différents consommateurs selon leurs morphotypes et leurs attentes émotionnelles. Dans cette thèse doctorale, nous proposons un système de recommandation orienté vers les consommateurs, qui peut être utilisé, comme un vendeur virtuel, à l’
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Dong, Min. "Development of an intelligent recommendation system to garment designers for designing new personalized products." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10025/document.

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Durant mes travaux en thèse, nous avons imaginé et poser les briques d'un système de recommandation intelligent (DIRS) orienté vers les créateurs de vêtements afin de les aider à créer des nouveaux produits personnalisés. Pour développer ce système, nous avons dans un premier temps identifié les composants clés du processus de création, puis nous avons créé un ensemble de bases de données pour collecter les données pertinentes. Dans un deuxième temps, nous avons acquis des données anthropométriques, recueilli la perception du concepteur à partir de ces mêmes morphotypes en utilisant un body sc
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Lohi, Abdolkhalil. "Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network." Thesis, University of Westminster, 2013. https://westminsterresearch.westminster.ac.uk/item/99060/investigation-of-an-intelligent-personalised-service-recommendation-system-in-an-ims-based-cellular-mobile-network.

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Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part o
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Chi, Cheng. "Personalized pattern recommendation system of men’s shirts based on precise body measurement." Electronic Thesis or Diss., Centrale Lille Institut, 2022. http://www.theses.fr/2022CLIL0003.

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Les systèmes commerciaux de recommandation de vêtements ont été largement utilisés dans l'industrie de l'habillement. Cependant, les recherches existantes sur la conception de vêtements numériques se sont concentrées sur les évolutions techniques du processus de conception virtuelle, avec peu de retours de métier provenant des designers. La coupe d'un vêtement joue un rôle important dans l'achat de celui-ci par le client. Afin de développer un vêtement correctement ajusté, les stylistes et les modélistes doivent ajuster le patron du vêtement plusieurs fois jusqu'à ce que le client soit satisfa
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Robles, Sebastian. "Business intelligence in Chile, recommendations to develop local applications." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/70831.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, June 2011.<br>"February 2010." Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 60).<br>The volume of information generated from enterprise applications is growing exponentially, and the cost of storage is decreasing rapidly. In addition, cloud-based applications, mobile devices and social networks are becoming relevant sources of unstructured data that provide essential information for strategic decisions making. Therefore, with time, enterprise dat
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Schröder, Anna Marie. "Unboxing The Algorithm : Understandability And Algorithmic Experience In Intelligent Music Recommendation Systems." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43841.

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After decades of black-boxing the existence of algorithms in technologies of daily need, users lack confidence in handling them. This thesis study investigates the use situation of intelligent music recommendation systems and explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience. In a Research-Through-Design approach, the project conducted focus user sessions and an expert interview to explore first-hand insights. The analysis showed that users had limited mental models so far but brought curiosity to learn. Explorative
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Lagerqvist, Gustaf, and Anton Stålhandske. "Recommendation systems for recruitment within an educational context." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42902.

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Alongside the evolution of the recruitment process, different types of recommendation systems have been developed. The purpose of this study is to investigate recommendation systems within educational contexts, successful implementations of recommendation system architecture patterns, and alternatives to previous experience when evaluating candidates. The study is conducted through two separate methods; A literature review with a qualitative approach and design science research methodology focused on design and development, demonstration and evaluation. The literature review shows that, for re
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Lesage, Laurent. "Data analysis for insurance : recommendation system based on a Multivariate Hawkes Process." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0048.

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L'objectif de la thèse est de construire un système de recommandation pour l'assurance. En observant le comportement et l'évolution d'un client dans le contexte assurantiel, les clients semblent modifier leur couverture d'assurance lorsqu'un événement important survient dans leur vie. Afin de prendre en compte l'influence des événements de la vie (par exemple, mariage, naissance, changement d'emploi) sur la sélection des garanties d'assurance auprès des clients, nous modélisons le système de recommandation avec un processus de Hawkes multivarié, qui comprend plusieurs fonctionnalités spécifiqu
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Books on the topic "Intelligent recommendation system"

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. &#x0D; The textbook in the field of training "Computer Science and Computer Engineering"
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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th
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Protasiewicz, Jarosław. Knowledge Recommendation Systems with Machine Intelligence Algorithms. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32696-7.

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Williams, Bradley P. ITS procurement: Analysis and recommendations. Virginia Transportation Research Council, 1994.

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America, IVHS. Federal IVHS program recommendations for fiscal years 1994 and 1995. IVHS America, 1992.

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Affairs, United States Congress Senate Committee on Homeland Security and Governmental. Ensuring full implementation of the 9/11 Commission's recommendations: Hearing before the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Tenth Congress, first session, January 7, 2007. U.S. G.P.O., 2009.

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Che, Natasha X. Intelligent Export Diversification: An Export Recommendation System with Machine Learning. International Monetary Fund, 2020.

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Che, Natasha X. Intelligent Export Diversification: An Export Recommendation System with Machine Learning. International Monetary Fund, 2020.

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Che, Natasha X. Intelligent Export Diversification: An Export Recommendation System with Machine Learning. International Monetary Fund, 2020.

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Trust For Intelligent Recommendation. Springer-Verlag New York Inc., 2013.

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Book chapters on the topic "Intelligent recommendation system"

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Padhi, Ashis Kumar, Ayog Mohanty, and Sipra Sahoo. "FindMoviez: A Movie Recommendation System." In Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6081-5_5.

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Frykowska, Adrianna, Izabela Zbieć, Patryk Kacperski, Peter Vesely, and Andrea Studenicova. "Movies Recommendation System." In Advances in Intelligent Networking and Collaborative Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29035-1_56.

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Rohatgi, Divya, Amit Patil, Shweta Patil, et al. "Mosaic Intelligent Event Recommendation System." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3817-5_48.

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Kumar, Keshav, Vatsal Sinha, Aman Sharma, M. Monicashree, M. L. Vandana, and B. S. Vijay Krishna. "AI-Assisted College Recommendation System." In Intelligent Sustainable Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2894-9_11.

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Kansal, Mahima, and Sohit Agarwal. "Enhanced Multimodal Recommendation System for Personalized Lifestyle Recommendations." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-700-7_5.

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Gund, Rohit, James Andro-Vasko, Doina Bein, and Wolfgang Bein. "Recommendation System Using MixPMF." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97652-1_32.

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Chaitra, D., V. R. Badri Prasad, and B. N. Vinay. "A Comprehensive Travel Recommendation System." In ICT with Intelligent Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4177-0_62.

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Sathya, A. R., and Alluri Raghav Varma. "Emotion-Based Song Recommendation System." In Intelligent Data Engineering and Analytics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6706-3_54.

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Singh, Gambhir, Anupam Kumar Saini, Vishal Pratap Singh, Muzammil Abedin, and Hammad Alam. "Movie recommendation system using AI." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003530176-63.

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Mgarbi, Hanae, Mohamed Yassin Chkouri, and Abderrahim Tahiri. "Recommendation System: Technical Study." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_23.

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Conference papers on the topic "Intelligent recommendation system"

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Pawar, Sahil, Ajinkya Pawar, Parth Pawar, and Jayashri Bagade. "Car Recommendation System." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828698.

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Pan, Tao. "Personalized Recommendation Service in University Libraries using Hybrid Collaborative Filtering Recommendation System." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721676.

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Shi, Hui. "Intelligent Recommendation Algorithm for Online Learning Resources Based on Recommendation System." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00106.

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Kokate, Vedant Sanjayrao, Yogesh Babulal Kunde, Vedant Prabhakar Patil, Pravin Futane, Suruchi Dedgaonkar, and Manohar Kodmelwar. "Cook Smart-An Intelligent Recipe Recommendation System." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915438.

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Zheng, Meng. "Intelligent Recommendation System for Online English Teaching Based on Artificial Intelligence." In 2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC). IEEE, 2024. https://doi.org/10.1109/iiotbdsc64371.2024.00080.

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Yu, Jihong, and Lin Wang. "Application Research of Intelligent Algorithm in Short Video Recommendation System Optimization Personalized Recommendation System." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729508.

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P, Suresh Babu, Chandu Beenapati, Pavan Kumar Reddy B, Subramanyam S, and Suresh Babu P. "Intelligent Plant Disease Recognition and Supplement Recommendation System." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10864049.

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G, Kiruthiga, Kamalesh S, Jegadeesan S, Manojkumar S, Kavinesh K K, and Ruban A S. "Intelligent Hotel Recommendation System Using Dynamic Graph Learning." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915530.

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Tomar, Nancy, Krish Sahu, Vishal Kumar, and Anubhava Srivastava. "Enhancing E-Commerce Experience Through Intelligent Recommendation System." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882291.

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Fatima, N. Sabiyath, N. Noor Alleema, C. Mahesh, R. Umanesan, K. Senthil, and P. Santhosh Kumar. "Adaptable Individualized Investment Recommendation System." In 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, 2024. https://doi.org/10.1109/icesic61777.2024.10846428.

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Reports on the topic "Intelligent recommendation system"

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Deppe, Sahar. AI-based reccomendation system for industrial training. Kompetenzzentrum Arbeitswelt.Plus, 2023. http://dx.doi.org/10.55594/vmtx7119.

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Recommendation systems have become a main part of e-learning, reshaping the landscape of digital education. In an era marked by the proliferation of online courses, diverse learning materials, and users with varying needs, these systems offer a dynamic solution. This paper explores recommendation techniques and their role in e-learning and web-based training, delving into their mechanisms, challenges, and opportunities. Moreover, future directions of these systems in e-learning, including the integration of artificial intelligent and emerging technologies, and the quest for transparency and pr
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Gehlhaus, Diana, Luke Koslosky, Kayla Goode, and Claire Perkins. U.S. AI Workforce: Policy Recommendations. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200087.

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This policy brief addresses the need for a clearly defined artificial intelligence education and workforce policy by providing recommendations designed to grow, sustain, and diversify the U.S. AI workforce. The authors employ a comprehensive definition of the AI workforce—technical and nontechnical occupations—and provide data-driven policy goals. Their recommendations are designed to leverage opportunities within the U.S. education and training system while mitigating its challenges, and prioritize equity in access and opportunity to AI education and AI careers.
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Chernavskikh, Vladislav, and Jules Palayer. Impact of Military Artificial Intelligence on Nuclear Escalation Risk. Stockholm International Peace Research Institute, 2025. https://doi.org/10.55163/fziw8544.

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Increasing integration of artificial intelligence (AI) into military systems has the potential to influence nuclear escalation even when that integration occurs outside nuclear weapon systems. Non-nuclear applications of military AI may compress decision-making timelines, potentially increasing miscalculation risks during a crisis. Opaque recommendations from an AI-powered decision-support system can bias a decision-maker towards acting, while autonomy in a system with counterforce potential may undermine strategic stability by threatening the integrity of second-strike capabilities. Such uses
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Faveri, Benjamin, Maureen Johnson-León, Prem Sylvester, et al. Towards A Global AI Auditing Framework: Assessment and Recommendations. Edited by Luis Adrián Castro-Quiroa, Eloísa Gacía-Canseco, Joan Hassan, et al. International Panel on the Information Environment (IPIE), 2025. https://doi.org/10.61452/zwed1485.

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A high-level précis of the Synthesis Report can be found in the Summary for Policymakers Recommendations for a Global AI Auditing Framework: Summary of Standards and Features. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditi
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McKinley, Catherine, Prem Sylvester, Benjamin Faveri, et al. Recommendations for a Global AI Auditing Framework: Summary of Standards and Features. Edited by Saiph Savage, Mona Sloam, Luis Adrián Castro-Quiroa, et al. International Panel on the Information Environment (IPIE), 2024. https://doi.org/10.61452/guyx7442.

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This Summary for Policymakers provides a high-level précis of the Synthesis Report Towards A Global AI Auditing Framework: Assessment and Recommendations. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditing, however, rarely i
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Barladym, Valentyna, A. V. Bruiaka, M. A. Bugaienko, et al. The Use of AI Tools and Services for the Professional Development of Teaching Staff. Institute for Digitalisation of Education of the NAES of Ukraіne, 2024. https://doi.org/10.33407/lib.naes.id/eprint/744000.

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The preprint (analytical materials) examines the process of using generative artificial intelligence in education; clarifies the role of artificial intelligence in the professional development of teaching staff; explores the pedagogical design of variable models of computer-oriented methodological systems for inquiry-based learning of natural and mathematical sciences using AI technologies; identifies the role of AI tools in the training of teaching staff; describes training in WebAR development with integrated machine learning: immersion methodology and intelligent educational experience; pro
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Legree, Peter J., and Philip D. Gillis. A Review of and Recommendations for Procedures Used to Evaluate the External Effectiveness of Intelligent Tutoring Systems. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada236625.

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Kim, Kyungmee, and Boulanin Vincent. Artificial Intelligence for Climate Security: Possibilities and Challenges. Stockholm International Peace Research Institute, 2023. http://dx.doi.org/10.55163/qdse8934.

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Recent advances in artificial intelligence (AI)—largely based on machine learning—offer possibilities for addressing climate-related security risks. AI can, for example, make disaster early-warning systems and long-term climate hazard modelling more efficient, reducing the risk that the impacts of climate change will lead to insecurity and conflict. This SIPRI Policy Report outlines the opportunities that AI presents for managing climate-related security risks. It gives examples of the use of AI in the field and delves into the problems—notably methodological and ethical—associated with the us
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Musser, Micah. Adversarial Machine Learning and Cybersecurity. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/2022ca003.

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Artificial intelligence systems are rapidly being deployed in all sectors of the economy, yet significant research has demonstrated that these systems can be vulnerable to a wide array of attacks. How different are these problems from more common cybersecurity vulnerabilities? What legal ambiguities do they create, and how can organizations ameliorate them? This report, produced in collaboration with the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center, presents the recommendations of a July 2022 workshop of experts to help answer these questions.
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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—su
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