Relevant bibliographies by topics / Intelligent recommendation systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Intelligent recommendation systems'

Author: Grafiati
Published: 2 June 2025
Last updated: 22 June 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 their specific applications in e-commerce. Next, from the viewpoint of consumer behavior theory, it systematically analyzes the impact of recommendation systems on consumer decision-making processes, purchasing behavior, and user satisfaction. It examines how recommendation systems influence consumer decisions and purchase intentions through mechanisms such as information overload, choice simplification, social recognition, and a sense of belonging. Additionally, the paper evaluates the applicability and effectiveness of various types of recommendation systems (e. g., personalized, contextual, and social recommendations) in different consumption scenarios. Findings indicate that intelligent recommendation systems significantly enhance shopping experiences and satisfaction while profoundly affecting purchasing decisions. This research provides theoretical guidance for designing recommendation systems on e-commerce platforms and offers new perspectives and methods for future consumer behavior studies. By delving into the complex interactions between recommendation systems and consumer behavior, it provides valuable insights for the intelligent transformation and user experience optimization of e-commerce platforms.
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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 systems identify the user’s emotional state and suggest songs that match or enhance their mood. Emotion-based music recommendation systems significantly enhance the way people experience music by improving emotional well-being, boosting user engagement, and broadening musical preferences. In this work, we propose an application called EMUS, an intelligent music recommendation system designed to suggest music based on the user’s emotional state.
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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 as the cold-start problem, data sparsity, bias of algorithms, the need for explainability of recommendations and privacy assurance are reviewed. Special attention is paid to the prospects of implementing recommendation systems in educational platforms. The importance of using hybrid and intelligent systems to effectively analyze user data and build recommendations tailored to individual needs is emphasized. The conclusion is drawn about further development of recommendation systems, which will be associated with the integration of the latest artificial intelligence technologies, optimization of computational resources and expansion of their application area in various digital ecosystems. The work can be useful for researchers, developers and practitioners working in the field of artificial intelligence and educational technologies.
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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 search, if a user visits a movie site for the first time, the site will have no previous history of that user. In such cases, the user can search for their movie recommendations based on genre, year of release, director or actor and their favorite movie itself to get a new movie recommendation. Keywords: Movie Recommendation Systems, Content-Based Filtering, Movie recommendation, machine learning project
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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. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.
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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, automated homework grading systems, and speech recognition technology. In terms of educational administration, it investigates the integration of intelligent student information systems, learning trajectory tracking systems, as well as course resource recommendation systems and teaching evaluation systems. The aim of the article is to furnish educators with valuable insights to enhance teaching quality and administrative efficiency, thereby promoting the intelligent development of private higher education institutions.
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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 unsuitable crop selections and ultimately helping raise overall productivity. The proposed work offers a web application that assists in classifying 22 crops based on various soil and environmental factors using two algorithms: SVM and Decision Trees. It analyzes the classifiers' accuracy using two performance metrics: the confusion matrix and the accuracy score. Farmers are better able to decide on the farming strategy they wish to use after utilizing the application.
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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 and validation on data collected from 506 questionnaires. The findings reveal that algorithmic transparency and perceived manipulation significantly impact the users' algorithmic avoidance in intelligent recommendation systems. The sense of being manipulated emerges as a crucial psychological factor leading to algorithmic avoidance, playing a complete mediating role in the influence of information quality, homogeneous recommendation, and algorithmic transparency on algorithmic avoidance.
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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 prototyping revealed that explanations could improve the algorithmic experience in music recommendation systems. Users could comprehend information the best when it was easy to access and digest, directly related to user behavior, and gave control to correct the algorithm. Concluding, trusting users with more transparent handling of algorithmic workings might make authentic recommendations from intelligent systems applicable in the long run.
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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 databases will become more valuable for business but also much harder to integrate, process and analyze. Business Intelligence software was instrumental in helping organizations to analyze information and provide reports to support business decision-making. Accordingly, BI applications evolved as enterprise information grew, hardware-processing capacities developed, and storage cost is being reduced significantly. In this paper, we will analyze the current BI world market and compare it with the Chilean market, in order to come up with business plan recommendations for local developers and systems integrators interested in capitalizing the opportunities generated by the global BI software market consolidation.<br>by Sebastian Robles.<br>S.M.in Engineering and Management
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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, to design an effective DRS, an intensive literature review was carried out on published studies of recommendation systems in the tourism domain. Second, the thesis proposes a model-based DRS, involving a two-step filtering feature selection method to remove irrelevant and redundant features and a Decision Tree (DT) classifier to offer interpretability, transparency and efficiency to tourists when they make decisions. To support high scalability, the system is evaluated with a huge body of real-world data collected from a case-study city. Destination choice models were developed and evaluated. Experimental results show that our proposed model-based DRS achieves good performance and can provide personalised recommendations with regard to tourist destinations that are satisfactory to intended users of the system. Third, the thesis proposes an ensemble-based DRS using weight hybrid and cascade hybrid. Three classification algorithms, DT, Support Vector Machines (SVMs) and Multi- Layer Perceptrons (MLPs), were investigated. Experimental results show that the bagging ensemble of MLP classifiers achieved promising results, outperforming baseline learners and other combiners. Lastly, the thesis also proposes an Adaptive, Responsive, Interactive Model-based User Interface (ARIM-UI) for DRS that allows tourists to interact with the recommended results easily. The proposed interface provides adaptive, informative and responsive information to tourists and improves the level of the user experience of the proposed system.
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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 preconditioned iterative methods. We tackled the problem of choosing suitable preconditioners for particular applications from a nontraditional point of view. We used the techniques and ideas in knowledge discovery and data mining to extract useful information and special features from unstructured sparse matrices and analyze the relationship between these features and the solving status of the spearse linear systems generated from these sparse matrices. We have designed an Intelligent Preconditioner Recommendation System, which can provide advice on choosing a high performance preconditioner as well as suitable parameters for a given sparse linear system. This work opened a new research direction for a very important topic in large scale high performance scientific computing. The performance of the various data mining algorithms applied in the recommendation system is directly related to the set of matrix features used in the system. We have extracted more than 60 features to represent a sparse matrix. We have proposed to use data mining techniques to predict some expensive matrix features like the condition number. We have also proposed to use the combination of the clustering and classification methods to predict the solving status of a sparse linear system. For the preconditioners with multiple parameters, we may predict the possible combinations of the values of the parameters with which a given sparse linear system may be successfully solved. Furthermore, we have proposed an algorithm to find out which preconditioners work best for a certain sparse linear system with what parameters.
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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’intérieur d’un système de vente de vêtements en ligne. Ce système a été développé par intégration de connaissance professionnelle des créateurs et des vendeurs et la perception des consommateurs sur les produits. En s’appuyant sur la connaissance de vente de vêtements, ce système propose des produits aux consommateurs spécifiques par exécuter successivement les trois modules de recommandation suivants, comprenant 1) le Module de Base de Données pour les Cas de Succès ; 2) le Module de Prévision du Marché ; 3) le Module de Recommandation utilisant la Connaissance. De plus, un autre module, appelé le Module de Mise à Jour de la Connaissance. Cette thèse présente une méthode originale de prévision d’un ou plusieurs profils de produits bien adaptés à un consommateur spécifique. Elle peut aider effectivement les consommateurs à effectuer des achats de vêtements sur l’Internet. En comparant avec les autres méthodes de prévision, la méthode proposée est plus robuste et plus interprétable en raison de sa capacité de traitement de l’incertitude<br>Garment purchasing through the Internet has become an important trend for consumers of all parts of the world. However, in various garment e-shopping systems, it systematically lacks personalized recommendations, like sales advisors in classical shops, in order to propose the most relevant products to different consumers according to their body shapes and fashion requirements. In this thesis, we propose a consumer-oriented recommendation system, which can be used inside a garment online shopping system like a virtual sales advisor. This system has been developed by integrating the professional knowledge of designers and shoppers and taking into account consumers’ perception on products. Following the shopping knowledge on garments, the proposed system recommends garment products to specific consumers by successively executing three modules, namely 1) the Successful Cases Database Module; 2) the Market Forecasting Module; 3) the Knowledge-based Recommendation Module. Also, another module, called the Knowledge Updating Module.This thesis presents an original method for predicting one or several relevant product profiles from a specific consumer profile. It can effectively help consumers to choose garments from the Internet. Compared with other prediction methods, the proposed method is more robust and interpretable owing to its capacity of treating uncertainty
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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 recommendation systems, a layered architecture built within a microservice ecosystem is successfully utilized and has multiple beneficial aspects such as improved scalability, maintainability and security. Through design science research methodology, this study shows a suggested approach to implementing a layered architecture in combination with KNN and hybrid filtering. To avoid the lapse of suitable candidates, caused by demanding previous experience, this study shows an alternative approach to recruitment, within an educational context, through the use of soft skills. Within the study, this approach is successfully used to evaluate and compare students, but the same approach could possibly be applied to evaluate and compare companies. Moving forward, this study could be further expanded by looking into possible biases arising as a result of using AI and choices made during this study, as well as weighting of student-attributes.
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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 of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically.
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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 scanner 3D et une procédure d'évaluation sensorielle. A la suite, une expérience instrumentale est conduite pour capturer les paramètres techniques des matières, nécessaires à leur représentation virtuelle en lien avec les morphotypes. Enfin, cinq expériences sensorielles sont réalisées pour capitaliser les connaissances des créateurs. Les données acquises servent à classer les morphotypes, à modéliser les relations entre morphotypes et facteurs de la création. A partir de ces modèles, nous avons mis en place une base de connaissances de la création mettant en œuvre une ontologie. Cette base de connaissances est mise à jour par un apprentissage dynamique au travers de nouveaux cas présentés en création. Ce système est utilisé au sein d’un nouveau processus de création. Ce processus peut s’effectuer autant de fois que nécessaire jusqu'à la satisfaction du créateur. Le système de recommandation proposé a été validé à l'aide de plusieurs cas réels<br>In my PhD research project, we originally propose a Designer-oriented Intelligent Recommendation System (DIRS) for supporting the design of new personalized garment products. For developing this system, we first identify the key components of a garment design process, and then set up a number of relevant databases, from which each design scheme can be formed. Second, we acquire the anthropometric data and designer’s perception on body shapes by using a 3D body scanning system and a sensory evaluation procedure. Third, an instrumental experiment is conducted for measuring the technical parameters of fabrics, and five sensory experiments are carried out in order to acquire designers’ knowledge. The acquired data are used to classify body shapes and model the relations between human bodies and the design factors. From these models, we set up an ontology-based design knowledge base. This knowledge base can be updated by dynamically learning from new design cases. On this basis, we put forward the knowledge-based recommendation system. This system is used with a newly developed design process. This process can be performed repeatedly until the designer’s satisfaction. The proposed recommendation system has been validated through a number of successful real design cases
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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 must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems.
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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 number of items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.
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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" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. &#x0D; Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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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 this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". &#x0D; The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. &#x0D; Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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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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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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Jing, Yanhui, and Guifu Jia. "Application of Artificial Intelligence (AI) Technology in Intelligent Recommendation of English Personalized Learning System." In 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS). IEEE, 2024. http://dx.doi.org/10.1109/aiars63200.2024.00090.

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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 privacy are highlighted. Additionally, a case study is discussed which focuses on providing a recommendation system in order to offer optimal courses for the employees of Weidmüller Interface GmbH &amp; Co. KG.
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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 auditing, however, rarely involve independent auditors, provide sufficient evidence, or account for global impacts. Policymakers urgently need a comprehensive global framework for AI audits that validates genuine benefits and risks. The IPIE’s Scientific Panel on Global Standards for AI Audits set out to independently establish global, cross-disciplinary scientific consensus on what makes an audit effective and trustworthy. The Panel consisted of 16 experts from computer science, the social sciences, and the humanities, with expertise spanning generative AI systems, algorithmic auditing, indigenous data sovereignty, data journalism, and the relationship between civil society, human rights, and AI.
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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 of AI raise the fundamental question of whether they introduce new risks, exacerbate existing ones or fundamentally alter the nature of nuclear escalation. Contextual and socio-technical factors that might affect nuclear escalation pathways can help to answer this question. Understanding these dynamics is essential for using current risk-reduction measures or developing new strategies to address nuclear escalation risks posed by military AI.
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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 involve independent auditors, provide sufficient evidence, or account for global impacts. Policymakers urgently need a comprehensive global framework for AI audits that validates genuine benefits and risks. The IPIE’s Scientific Panel on Global Standards for AI Audits set out to independently establish global, cross-disciplinary scientific consensus on what makes an audit effective and trustworthy. The Panel consisted of 16 experts from computer science, the social sciences, and the humanities, with expertise spanning generative AI systems, algorithmic auditing, indigenous data sovereignty, data journalism, and the relationship between civil society, human rights, and AI.
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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; provides recommendations for using Microsoft Copilot Chat in teacher training; investigates the issue of using AI in the preparation of education science specialists; presents the experience of implementing AI in teaching the discipline “Information and Communication Technologies for Teaching, Management, and Support of Scientific and Educational Research”; and characterizes the ethical aspects of using AI in education. The preprint (analytical materials) can be used in the professional development of teaching staff by teachers, lecturers, postgraduate students, doctoral candidates, and researchers interested in the implementation of AI tools and services in education.
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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 use of AI for climate security. The report concludes with recommendations for policymakers and researchers who are active in the area of climate security or who use AI for sustainability.
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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—such as ChatGPT or Gemini—capable of producing text, images, or decisions based on prompts. However, it is the third wave, embodied by AI agents, that poses the greatest challenge: software capable of autonomous action, making decisions on behalf of users, interacting across platforms, and executing tasks with minimal human oversight.
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