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Добірка наукової літератури з теми "Intelligent recommendation system"

Автор: Grafiati

Опубліковано: 16 березня 2023

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Статті в журналах з теми "Intelligent recommendation system"

Kathait, ShailendraSingh, Shubhrita Tiwari, and PiyushKumar Singh. "INTELLIGENT RECOMMENDATION SYSTEM." International Journal of Advanced Research 5, no. 2 (February 28, 2017): 1649–56. http://dx.doi.org/10.21474/ijar01/3328.

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Mishra, Ikshita, Ankita Sharma, and Tanuj Deria. "Intelligent Tourist Recommendation System." IJARCCE 6, no. 4 (April 30, 2017): 384–91. http://dx.doi.org/10.17148/ijarcce.2017.6474.

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Rtili, Mohammed Kamal, Ali Dahmani, and Mohamed Khaldi. "Recommendation System Based on the Learners' Tracks in an Intelligent Tutoring System." Journal of Advances in Computer Networks 2, no. 1 (2014): 40–43. http://dx.doi.org/10.7763/jacn.2014.v2.79.

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Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 27, 2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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Анотація:

The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.

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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 (May 31, 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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Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (August 18, 2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

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., Jay Borade. "INTELLIGENT AGENT FOR TOURISM RECOMMENDATION SYSTEM." International Journal of Research in Engineering and Technology 07, no. 04 (April 25, 2018): 39–46. http://dx.doi.org/10.15623/ijret.2018.0704007.

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Cui, Xiaoyue. "An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database." Mobile Information Systems 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3334047.

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Анотація:

In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.

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Mao, Qingqing, Aihua Dong, Qingying Miao, and Lu Pan. "Intelligent Costume Recommendation System Based on Expert System." Journal of Shanghai Jiaotong University (Science) 23, no. 2 (April 2018): 227–34. http://dx.doi.org/10.1007/s12204-018-1933-x.

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Chen, Qing Zhang, Yu Jie Pei, Yan Jin, and Li Yan Zhang. "Research on Intelligent Recommendation Method and its Application on Internet Bookstore." Advanced Materials Research 121-122 (June 2010): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.447.

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As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

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Дисертації з теми "Intelligent recommendation system"

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
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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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
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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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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Chi, Cheng. "Personalized pattern recommendation system of men’s shirts based on precise body measurement." Electronic Thesis or Diss., Centrale Lille Institut, 2022. http://www.theses.fr/2022CLIL0003.

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Les systèmes commerciaux de recommandation de vêtements ont été largement utilisés dans l'industrie de l'habillement. Cependant, les recherches existantes sur la conception de vêtements numériques se sont concentrées sur les évolutions techniques du processus de conception virtuelle, avec peu de retours de métier provenant des designers. La coupe d'un vêtement joue un rôle important dans l'achat de celui-ci par le client. Afin de développer un vêtement correctement ajusté, les stylistes et les modélistes doivent ajuster le patron du vêtement plusieurs fois jusqu'à ce que le client soit satisfait. Actuellement, le modélisme traditionnel présente trois inconvénients majeurs : 1) il est très long et inefficace, 2) il repose trop sur des concepteurs expérimentés, 3) la relation entre la forme du corps humain et le vêtement n'est pas pleinement explorée. Dans la pratique, le styliste joue un rôle clé dans la réussite du processus de conception. Il est nécessaire d'intégrer les connaissances et l'expérience du styliste dans les systèmes actuels de CAD de vêtements afin de fournir rapidement une solution de conception réalisable, centrée sur l'homme et à faible coût, pour chaque besoin personnalisé. En outre, les services basés sur les données, tels que les systèmes de recommandation, la classification des formes corporelles, la modélisation du corps en 3D et l'évaluation de l'ajustement des vêtements, devraient être intégrés dans le système de CAD de l'habillement afin d'améliorer l'efficacité du processus de conception.Sur la base de ces besoins, cette thèse propose un système de recommandation intelligent composé de modèles de vêtements ajustables pour conduire à la conception de vêtements personnalisés. Le système fonctionne en combinaison avec un nouveau processus de conception nouvellement développé, à savoir l'identification de la forme du corps humain - la recommandation d'une solution de conception - la représentation virtuelle 3D et l'évaluation - l'ajustement des paramètres de conception. Ce processus peut être répété jusqu'à ce que l'utilisateur soit satisfait. Le système de recommandation proposé a été validé par quelques cas pratiques de conception réussis
Commercial garment recommendation systems have been widely used in the apparel industry. However, existing research on digital garment design has focused on the technical development of the virtual design process, with little knowledge of traditional designers. The fit of a garment plays a significant role in whether a customer purchases that garment. In order to develop a well-fitting garment, designers and pattern makers should adjust the garment pattern several times until the customer is satisfied. Currently, there are three main disadvantages of traditional pattern-making: 1) it is very time-consuming and inefficient, 2) it relies too much on experienced designers, 3) the relationship between the human body shape and the garment is not fully explored. In practice, the designer plays a key role in a successful design process. There is a need to integrate the designer's knowledge and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modelling, and garment fit assessment should be integrated into the apparel CAD system to improve the efficiency of the design process.Based on the above issues, in this thesis, a fit-oriented garment pattern intelligent recommendation system is proposed for supporting the design of personalized garment products. The system works in combination with a newly developed design process, i.e. body shape identification - design solution recommendation - 3D virtual presentation and evaluation - design parameter adjustment. This process can be repeated until the user is satisfied. The proposed recommendation system has been validated by some successful practical design cases

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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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Анотація:

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, June 2011.
"February 2010." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 60).
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.
by Sebastian Robles.
S.M.in Engineering and Management

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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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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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Sun, Runpu. "Using Social Media Intelligence to Support Business Knowledge Discovery and Decision Making." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145394.

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The new social media sites - blogs, micro-blogs, and social networking sites, among others - are gaining considerable momentum to facilitate collaboration and social interactions in general. These sites provide a tremendous asset for understanding social phenomena by providing a wide availability of novel data sources. Recent estimates suggest that social media sites are responsible for as much as one third of new Web content, in the forms of social networks, comments, trackbacks, advertisements, tags, etc. One critical and immediate challenge facing the MIS researchers then becomes - how to effectively utilize this huge wealth of social media data, to facilitate business knowledge discovery and decision making.Among these available data sources, social networks constitute the backbone of almost all social media sites. These network structures provide a rich description of the social scenes and contexts, which is helpful for us to address the above challenge. In this dissertation, I have primarily employed the probabilistic network models, to study various social network related problems arose from the use of social media services. In Chapter 2 and Chapter 3, I studied how information overload can affect the efficiency of information diffusion in online social networks (Delicious.com and Digg.com). Novel diffusion model were proposed to model the observed information overload. The models and their extensions are thoroughly evaluated by solving the Influence Maximization problem related to information diffusion and viral marketing applications. In Chapter 4, I studied the information overload in a micro-blogging application (Twitter.com) using a design science methodology. A content recommendation framework was proposed to help micro-blogging users to efficiently identify quality emergency news feeds. Chapter 5 presents a novel burst detection algorithm concerning identifying and analyzing correlated burst patterns by considering multiple inputs (data streams) that co-evolve over time. The algorithm was later used for discovering burst keywords/tag pairs from online social communities, which are strong indicators of emerging or changing user interests.Chapter 6 concludes this dissertation by highlighting major research contributions and future directions.

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Книги з теми "Intelligent recommendation system"

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: 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. 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. 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. ru: 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". 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. 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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Williams, Bradley P. ITS procurement: Analysis and recommendations. Charlottesville, Va: Virginia Transportation Research Council, 1994.

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

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service), SpringerLink (Online, ed. Modeling Intention in Email: Speech Acts, Information Leaks and Recommendation Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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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. Washington: U.S. G.P.O., 2009.

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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. Washington: U.S. G.P.O., 2009.

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

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

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

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Частини книг з теми "Intelligent recommendation system"

Padhi, Ashis Kumar, Ayog Mohanty, and Sipra Sahoo. "FindMoviez: A Movie Recommendation System." In Intelligent Systems, 49–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6081-5_5.

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Frykowska, Adrianna, Izabela Zbieć, Patryk Kacperski, Peter Vesely, and Andrea Studenicova. "Movies Recommendation System." In Advances in Intelligent Networking and Collaborative Systems, 579–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29035-1_56.

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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, 141–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2894-9_11.

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Gund, Rohit, James Andro-Vasko, Doina Bein, and Wolfgang Bein. "Recommendation System Using MixPMF." In Advances in Intelligent Systems and Computing, 263–68. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97652-1_32.

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Chaitra, D., V. R. Badri Prasad, and B. N. Vinay. "A Comprehensive Travel Recommendation System." In ICT with Intelligent Applications, 623–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4177-0_62.

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Zhao, Ziyin, Lei Zhou, and Tongtong Zhang. "Intelligent Recommendation System for Eyeglass Design." In Advances in Intelligent Systems and Computing, 402–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20441-9_42.

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Jain, Kartik Narendra, Vikrant Kumar, Praveen Kumar, and Tanupriya Choudhury. "Movie Recommendation System: Hybrid Information Filtering System." In Intelligent Computing and Information and Communication, 677–86. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_66.

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Forestiero, Agostino. "AIRS: Ant-Inspired Recommendation System." In Advances in Intelligent Systems and Computing, 213–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11310-4_19.

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Lekshmi Priya, T., and Harikumar Sandhya. "Matrix Factorization for Recommendation System." In Advances in Intelligent Systems and Computing, 267–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_22.

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Voggu, Suman Venkata Sai, Yuvraj Singh Champawat, Swaraj Kothari, and B. K. Tripathy. "Recommendation System Using Community Identification." In Advances in Intelligent Systems and Computing, 125–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1286-5_11.

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Тези доповідей конференцій з теми "Intelligent recommendation system"

Toskova, Asya, and Georgi Penchev. "Intelligent game recommendation system." In THERMOPHYSICAL BASIS OF ENERGY TECHNOLOGIES (TBET 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042063.

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Stan, Cristiana, and Irina Mocanu. "An Intelligent Personalized Fashion Recommendation System." In 2019 22nd International Conference on Control Systems and Computer Science (CSCS). IEEE, 2019. http://dx.doi.org/10.1109/cscs.2019.00042.

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Choi, Chang, Miyoung Cho, Junho Choi, Myunggwon Hwang, Jongan Park, and Pankoo Kim. "Travel Ontology for Intelligent Recommendation System." In 2009 Third Asia International Conference on Modelling & Simulation. IEEE, 2009. http://dx.doi.org/10.1109/ams.2009.75.

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ZHANG, J., X. ZENG, L. KOEHL, and M. DONG. "CONSUMER-ORIENTED INTELLIGENT GARMENT RECOMMENDATION SYSTEM." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0140.

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Tu, Qingqing, and Le Dong. "An Intelligent Personalized Fashion Recommendation System." In 2010 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2010. http://dx.doi.org/10.1109/icccas.2010.5581949.

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Saxena, Rohan, Maheep Chaudhary, Chandresh Kumar Maurya, and Shitala Prasad. "An Intelligent Recommendation-cum-Reminder System." In CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD). New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3493700.3493724.

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Ong, Kyle, Su-Cheng Haw, and Kok-Why Ng. "Deep Learning Based-Recommendation System." In CIIS 2019: 2019 The 2nd International Conference on Computational Intelligence and Intelligent Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3372422.3372444.

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Wong, Tak-Lam. "An intelligent recommendation system using preference regularization." In 2014 14th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2014. http://dx.doi.org/10.1109/isda.2014.7066284.

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Meehan, Kevin, Tom Lunney, Kevin Curran, and Aiden McCaughey. "Context-aware intelligent recommendation system for tourism." In 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops 2013). IEEE, 2013. http://dx.doi.org/10.1109/percomw.2013.6529508.

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Uppada, Santosh Kumar, Dani Prakash Esukapalli, and B. Sivaselvan. "MitrApp: An Intelligent Recommendation System For Counselling." In 2020 IEEE 4th Conference on Information & Communication Technology (CICT). IEEE, 2020. http://dx.doi.org/10.1109/cict51604.2020.9312107.

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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, October 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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Legree, Peter J., and Philip D. Gillis. A Review of and Recommendations for Procedures Used to Evaluate the External Effectiveness of Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada236625.

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Reeb, Tyler D., and Stacey Park. Trade and Transportation Talent Pipeline Blueprints: Building UniversityIndustry Talent Pipelines in Colleges of Continuing and Professional Education. Mineta Transportation Institute, February 2023. http://dx.doi.org/10.31979/mti.2023.2144.

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The rapid adoption of transformational technologies along with other economic and cultural shifts, have created a gap between workers and the skills and knowledge necessary for in-demand occupations. Trade and Transportation Talent Pipeline Blueprints: Building University-Industry Talent Pipelines in Colleges of Continuing and Professional Education identifies the steps required to build talent pipelines that target in-demand trade and transportation occupations requiring specific degrees, certificates, and non-credit professional development. This report provides a literature review and labor market data analysis. It also includes documentation of methodology in planning a pilot program for Colleges of Professional and Continuing Education housed within each of the 23 California State University campuses. The recommendations guide the colleges to develop talent pipelines to empower trade and transportation employers to play a more central role in addressing skills gaps and other critical workforce development needs in working partnerships with postsecondary education and training providers. The report concludes with a recommended university-industry Intelligent Transportation Systems (ITS) Talent Pipeline pilot program.

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Pyta, V., Bharti Gupta, Shaun Helman, Neale Kinnear, and Nathan Stuttard. Update of INDG382 to include vehicle safety technologies. TRL, July 2020. http://dx.doi.org/10.58446/thco7462.

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Driving is one of the riskiest work tasks, accounting for around one third of fatal crashes in the UK. Organisations are expected to manage work-related road safety (WRRS) in the same way that they manage other health and safety risks. The Health and Safety Executive (HSE) and Department for Transport (DFT) issue joint guidance on this in INDG382 ‘Driving at work: managing work-related road safety’. HSE and DFT were seeking to update INDG382 to include reference to vehicle safety technologies that could enable employers to monitor safety related events or driver behaviours, to support learning and safety improvements. They commissioned TRL to - Conduct a literature review focused on evaluations of the impact of these technologies on work-related road safety (specifically, crash risk) Lead in-depth interviews with representatives of organisations who had implemented technology-based safety monitoring in their fleet and stakeholders and experts who provided further insights into factors affecting successful implementation. TRL found that telematics systems, drowsiness and distraction recognition systems, and collision warning systems have significant potential safety benefits, but rigorous published evaluation of safety-focused telematics in the fleet context is limited. There is good evidence for the safety benefits of intelligent speed assist in private and fleet vehicles. Successful implementation relies on procuring systems that match needs, managing the potential for data to overwhelm and embedding monitoring and driver feedback within good management systems and strong safety leadership. This report provides recommendations for updating guidance for organisations considering implementing vehicle safety monitoring technologies (telematics).

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Bourrier, Mathilde, Michael Deml, and Farnaz Mahdavian. Comparative report of the COVID-19 Pandemic Responses in Norway, Sweden, Germany, Switzerland and the United Kingdom. University of Stavanger, November 2022. http://dx.doi.org/10.31265/usps.254.

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The purpose of this report is to compare the risk communication strategies and public health mitigation measures implemented by Germany, Norway, Sweden, Switzerland, and the United Kingdom (UK) in 2020 in response to the COVID-19 pandemic based on publicly available documents. The report compares the country responses both in relation to one another and to the recommendations and guidance of the World Health Organization where available. The comparative report is an output of Work Package 1 from the research project PAN-FIGHT (Fighting pandemics with enhanced risk communication: Messages, compliance and vulnerability during the COVID-19 outbreak), which is financially supported by the Norwegian Research Council's extraordinary programme for corona research. PAN-FIGHT adopts a comparative approach which follows a “most different systems” variation as a logic of comparison guiding the research (Przeworski & Teune, 1970). The countries in this study include two EU member States (Sweden, Germany), one which was engaged in an exit process from the EU membership (the UK), and two non-European Union states, but both members of the European Free Trade Association (EFTA): Norway and Switzerland. Furthermore, Germany and Switzerland govern by the Continental European Federal administrative model, with a relatively weak central bureaucracy and strong subnational, decentralised institutions. Norway and Sweden adhere to the Scandinavian model—a unitary but fairly decentralised system with power bestowed to the local authorities. The United Kingdom applies the Anglo-Saxon model, characterized by New Public Management (NPM) and decentralised managerial practices (Einhorn & Logue, 2003; Kuhlmann & Wollmann, 2014; Petridou et al., 2019). In total, PAN-FIGHT is comprised of 5 Work Packages (WPs), which are research-, recommendation-, and practice-oriented. The WPs seek to respond to the following research questions and accomplish the following: WP1: What are the characteristics of governmental and public health authorities’ risk communication strategies in five European countries, both in comparison to each other and in relation to the official strategies proposed by WHO? WP2: To what extent and how does the general public’s understanding, induced by national risk communication, vary across five countries, in relation to factors such as social capital, age, gender, socio-economic status and household composition? WP3: Based on data generated in WP1 and WP2, what is the significance of being male or female in terms of individual susceptibility to risk communication and subsequent vulnerability during the COVID-19 outbreak? WP4: Based on insight and knowledge generated in WPs 1 and 2, what recommendations can we offer national and local governments and health institutions on enhancing their risk communication strategies to curb pandemic outbreaks? WP5: Enhance health risk communication strategies across five European countries based upon the knowledge and recommendations generated by WPs 1-4. Pre-pandemic preparedness characteristics All five countries had pandemic plans developed prior to 2020, which generally were specific to influenza pandemics but not to coronaviruses. All plans had been updated following the H1N1 pandemic (2009-2010). During the SARS (2003) and MERS (2012) outbreaks, both of which are coronaviruses, all five countries experienced few cases, with notably smaller impacts than the H1N1 epidemic (2009-2010). The UK had conducted several exercises (Exercise Cygnet in 2016, Exercise Cygnus in 2016, and Exercise Iris in 2018) to check their preparedness plans; the reports from these exercises concluded that there were gaps in preparedness for epidemic outbreaks. Germany also simulated an influenza pandemic exercise in 2007 called LÜKEX 07, to train cross-state and cross-department crisis management (Bundesanstalt Technisches Hilfswerk, 2007). In 2017 within the context of the G20, Germany ran a health emergency simulation exercise with WHO and World Bank representatives to prepare for potential future pandemics (Federal Ministry of Health et al., 2017). Prior to COVID-19, only the UK had expert groups, notably the Scientific Advisory Group for Emergencies (SAGE), that was tasked with providing advice during emergencies. It had been used in previous emergency events (not exclusively limited to health). In contrast, none of the other countries had a similar expert advisory group in place prior to the pandemic. COVID-19 waves in 2020 All five countries experienced two waves of infection in 2020. The first wave occurred during the first half of the year and peaked after March 2020. The second wave arrived during the final quarter. Norway consistently had the lowest number of SARS-CoV-2 infections per million. Germany’s counts were neither the lowest nor the highest. Sweden, Switzerland and the UK alternated in having the highest numbers per million throughout 2020. Implementation of measures to control the spread of infection In Germany, Switzerland and the UK, health policy is the responsibility of regional states, (Länders, cantons and nations, respectively). However, there was a strong initial centralized response in all five countries to mitigate the spread of infection. Later on, country responses varied in the degree to which they were centralized or decentralized. Risk communication In all countries, a large variety of communication channels were used (press briefings, websites, social media, interviews). Digital communication channels were used extensively. Artificial intelligence was used, for example chatbots and decision support systems. Dashboards were used to provide access to and communicate data.

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).

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