Relevant bibliographies by topics / Intelligent recommendation systems

Contents

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

Academic literature on the topic 'Intelligent recommendation systems'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Intelligent recommendation systems"

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Resnick, Marc L., Sheryda Pompa, Isaac Korn, and Omar Castillo. "Persuasive Design Through Intelligent Recommendation Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 13 (2004): 1503–7. http://dx.doi.org/10.1177/154193120404801307.

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Zunying, Xie. "Analysis of Intelligent Recommendation Systems and Consumer Behavior Theories on E-Commerce Platforms." Philosophy and Social Science 1, no. 6 (2024): 10–15. https://doi.org/10.62381/p243602.

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This study explores the interplay between intelligent recommendation systems and consumer behavior theories on e-commerce platforms. With the rapid growth of e-commerce, intelligent recommendation systems have become vital tools for enhancing user experience and boosting sales. While much literature addresses the technical implementation and algorithm optimization of these systems, research from the perspective of consumer behavior theory is limited. This paper first reviews the fundamental principles and technological evolution of recommendation systems, summarizing common algorithms and thei
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Sohel, Shaik, Vanukuri Manideepa, Alla Sai Pavan, Danaboina Vamsi Krishna, and KRMC Sekhar. "EMUS: An Intelligent Music Recommendation System." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 751–55. https://doi.org/10.54660/.ijmrge.2025.6.2.751-755.

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Music plays a prominent role in various aspects of human life, culture, and society by influencing emotions, strengthening social bonds, preserving traditions, and shaping personal and collective identities. As AI emerges as a powerful tool to automate various tasks, music recommendation systems have become an integral part of this transformation. These systems automatically generate personalized music playlists for users based on their mood and listening behavior. By analyzing factors like facial expressions, voice tone, text input, and listening history, AI-driven music recommendation system
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Iklassova, K. Е., A. K. Shaikhanova, M. Zh Bazarova, R. M. Tashibayev, and A. S. Kazanbayeva. "REVIEW OF RECOMMENDER SYSTEMS: MODELS AND PROSPECTS FOR USE IN EDUCATIONAL PLATFORMS." Bulletin of Shakarim University. Technical Sciences, no. 1(17) (March 29, 2025): 12–20. https://doi.org/10.53360/2788-7995-2025-1(17)-2.

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Recommendation systems play a key role in the digital environment, providing personalized recommendations in online stores, streaming services, social networks, and educational platforms. This paper presents a comprehensive review of recommendation system models, including content and collaborative filtering, hybrid approaches, and state-of-the-art algorithms based on deep learning, reinforcement learning, and graph neural networks. The advantages and disadvantages of different methods, their accuracy, performance, scalability and adaptability to new data are analyzed. The main challenges such
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Hirolikar, D. S., Ajinkya Satuse, Omkar Bhalerao, Pavan Pawar, and Hrithik Thorat. "Intelligent Movie Recommendation System Using AI and ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 611–22. http://dx.doi.org/10.22214/ijraset.2022.42255.

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Abstract: Recommender system are systems which provide you with a similar type of products or solutions and results, you are looking for. For example, if you go to a Clothing shop, you ask for a T-shirt with different designs or different colors, Then the shopkeeper recommends you with different colors. This recommending task for websites is done by recommending systems. A recommendation engine uses several algorithms to filter data and then recommends the most relevant items to consumers. A Movie Recommender system will recommend the most relevant and connected movie for the given category of
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Jia, Yu Bo, Qian Qian Ding, Dan Li Liu, Jian Feng Zhang, and Yun Long Zhang. "Collaborative Filtering Recommendation Technology Based on Genetic Algorithm." Applied Mechanics and Materials 599-601 (August 2014): 1446–52. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1446.

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Huang, Zhao, and Pavel Stakhiyevich. "A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users." Complexity 2021 (February 27, 2021): 1–19. http://dx.doi.org/10.1155/2021/8826833.

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Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. Th
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Wang, Peilian, and Hui Xie. "Application and Exploration of Artificial Intelligence in Teaching and Learning in Private Colleges and Universities." World Journal of Education and Humanities 6, no. 3 (2024): p26. http://dx.doi.org/10.22158/wjeh.v6n3p26.

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The rapid advancement of artificial intelligence technology across various sectors has sparked profound transformation in the field of education, particularly in the realms of pedagogy and administration within private higher education institutions. The discourse delves into the specific applications of AI educational aids in both classroom instruction and post-class learning, encompassing intelligent tutoring systems, virtual laboratories, and personalized learning recommendation systems, among others. Furthermore, it addresses the utilization of AI-driven question-answering systems, automate
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Chunduri, Sreya, Harry Raj, and Narendra V. G. "Intelligent Systems for Crop Recommendation using Machine Learning." WSEAS TRANSACTIONS ON COMPUTERS 24 (January 10, 2025): 14–19. https://doi.org/10.37394/23205.2025.24.2.

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Given the soil and climate, information is of utmost importance in predicting which crop is best suited. Crops can now be grown with higher precision by analyzing data regarding temperature, humidity, soil conditions, and the chemical makeup of the soil, all of which impact crop growth. This is one facet of Precision Agriculture. Precision agriculture is a contemporary farming approach that uses scientific findings on the types, properties, and yields of soil. It guides farmers in selecting the most suitable crops tailored to their specific site conditions, reducing the chance of making unsuit
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Cheng, Xiao, and Guochao Peng. "Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems." Journal of Global Information Management 32, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/jgim.352857.

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Through an exploration of the underlying mechanisms driving users' algorithmic avoidance in intelligent recommendation systems, this study aims to facilitate a positive interaction between users and technology, providing theoretical guidance for the efficient operations of enterprises using intelligent recommendation systems. The research integrates the theories of information ecology and psychological resistance, establishing a model of influencing factors on users' algorithmic avoidance in intelligent recommendation systems. Utilizing a structural equation model, the study conducts analysis
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Dissertations / Theses on the topic "Intelligent recommendation systems"

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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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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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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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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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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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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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Khoshkangini, Reza. "Personalized Game Content Generation and Recommendation for Gamified Systems." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424854.

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Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive mu
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Alsalama, Ahmed. "A Hybrid Recommendation System Based on Association Rules." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1250.

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Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small
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Books on the topic "Intelligent recommendation systems"

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

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Bhuiyan, Touhid. Trust for Intelligent Recommendation. Springer, 2013.

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Bhuiyan, Touhid. Trust for Intelligent Recommendation. Springer London, Limited, 2013.

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Jain, Lakhmi C., George A. Tsihrintzis, and Maria Virvou. Multimedia Services in Intelligent Environments: Recommendation Services. Springer, 2015.

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Book chapters on the topic "Intelligent recommendation systems"

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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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Garcia, Luís P. F., Felipe Campelo, Guilherme N. Ramos, Adriano Rivolli, and André C. P. de L. F. de Carvalho. "Evaluating Clustering Meta-features for Classifier Recommendation." In Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91702-9_30.

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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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Huang, Hua-Hong, Sheng-Min Chiu, Yi-Chung Chen, and Chiang Lee. "Group Trip Recommendation Systems." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03402-3_27.

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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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Maâtallah, Majda, and Hassina Seridi-Bouchelaghem. "Multi-context Recommendation in Technology Enhanced Learning." In Intelligent Tutoring Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_137.

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Felfernig, Alexander, Monika Mandl, Stefan Schippel, Monika Schubert, and Erich Teppan. "Adaptive Utility-Based Recommendation." In Trends in Applied Intelligent Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13022-9_64.

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Liu, Wenjun. "Community Education Course Recommendation Based on Intelligent Recommendation Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25128-4_244.

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Paul, Dip, and Subhradeep Kundu. "A Survey of Music Recommendation Systems with a Proposed Music Recommendation System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7403-6_26.

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Conference papers on the topic "Intelligent recommendation systems"

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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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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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Baadou, Sana, Salim Lafdoul, and Ahmed Bendahmane. "Intelligent Recommendation Systems: Literature Review on Recommendation Techniques and Their Use in Education." In 2024 Mediterranean Smart Cities Conference (MSCC). IEEE, 2024. http://dx.doi.org/10.1109/mscc62288.2024.10697042.

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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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Reddy, Y. V. Bhaskar, C. Satya Kumar, Parise Sai Niteesh, Gunda Ravi Teja, P. Ashok Reddy, and M. Babu Reddy. "EAPCET College List Recommendation System." In 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC). IEEE, 2025. https://doi.org/10.1109/esic64052.2025.10962751.

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Maheswari, M., Mudduluru Gayathri, Mopuru Yoshna Reddy, S. L. Jany Shabu, J. Refonaa, and Dhamodaran. "Collaborative Recommendation Systems for E-Learning Sources." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076501.

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Vani, K. Suvarna, Praneeth Vallabhaneni, and Hema Yalavarthi. "Diabetes Prediction and Ayurvedic Food Recommendation System." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007437.

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Bai, Wen, Yu Tang, and Ji Wang. "Research on Intelligent Data Mining in Ecotourism Market Trend Forecasting Intelligent Recommendation System." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10986857.

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Wang, Sufang. "Intelligent Recommendation of Open Education Teaching Resources based on Hybrid Collaborative Recommendation Algorithm with Large Language Model." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934397.

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Özlü, Özgür Anıl, Günce Keziban Orman, and Sultan N. Turhan. "Exploring Graph-Based Techniques in Job Recommendation Systems." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705169.

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Reports on the topic "Intelligent recommendation systems"

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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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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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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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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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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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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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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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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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