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    Wyświetlanie 1-14 z 14
    Tytuł:
    The recommendation algorithm for an online art gallery
    Autorzy:
    Karwowski, W.
    Sosnowska, J.
    Rusek, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/94759.pdf
    Data publikacji:
    2018
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    algorithms
    recommender system
    collaborative filtering
    Opis:
    The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.
    Źródło:
    Information Systems in Management; 2018, 7, 2; 108-119
    2084-5537
    2544-1728
    Pojawia się w:
    Information Systems in Management
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new approach to image-based recommender systems with the application of heatmaps maps
    Autorzy:
    Woldan, Piotr
    Duda, Piotr
    Cader, Andrzej
    Laktionov, Ivan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201330.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    feature extraction
    recommender system
    heatmap
    Opis:
    One of the fundamental issues of modern society is access to interesting and useful content. As the amount of available content increases, this task becomes more and more challenging. Our needs are not always formulated in words; sometimes we have to use complex data types like images. In this paper, we consider the three approaches to creating recommender systems based on image data. The proposed systems are evaluated on a real-world dataset. Two case studies are presented. The first one presents the case of an item with many similar objects in a database, and the second one with only a few similar items
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 63--72
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms
    Autorzy:
    Pawłowska, Justyna
    Rydzewska, Klara
    Wierzbicki, Adam
    Powiązania:
    https://bibliotekanauki.pl/articles/2201326.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    recommender system
    cognitive limitations
    aging
    e-commerce
    Opis:
    Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 73--94
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of multi-criteria analysis based on individual psychological profile for recommender systems
    Autorzy:
    Rafalak, M.
    Granat, J.
    Wierzbicki, A. P.
    Powiązania:
    https://bibliotekanauki.pl/articles/305393.pdf
    Data publikacji:
    2016
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    recommender system
    multi-criteria analysis
    user profiling
    Opis:
    This paper presents a novel approach for user classification exploiting multi- criteria analysis. This method is based on measuring the distance between an observation and its respective Pareto front. The obtained results show that the combination of the standard KNN classification and the distance from Pareto fronts gives satisfactory classification accuracy – higher than the accuracy ob- tained for each of these methods applied separately. Conclusions from this study may be applied in recommender systems where the proposed method can be implemented as the part of the collaborative filtering algorithm.
    Źródło:
    Computer Science; 2016, 17 (4); 503-517
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recommender system for navigation safety: requirements and methodology
    Autorzy:
    Shilov, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/117388.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    navigational safety
    navigation safety
    methodology
    recommender system
    maneuverability
    recommender system for navigation safety
    advanced driver assistance systems
    Safety at Sea
    Opis:
    Low maneuverability of ships together with growing intensity of marine traffic result in new challenges related to navigation safety. This paper reports a research aimed at design of methodology of operation of recommender systems for navigation safety. First, a specification of requirements to systems of the considered class has been carried out. Based on these, the major principles of functioning of such systems have been defined. The principles were a basis for development of the mentioned above methodology, which is based on the usage of context patterns and characterized by the presence of feedback to update the system’s knowledge base.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 405-410
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Resource optimisation in cloud computing: comparative study of algorithms applied to recommendations in a big data analysis architecture
    Autorzy:
    Ndayikengurukiye, Aristide
    Ez-Zahout, Abderrahmane
    Aboubakr, Akou
    Charkaoui, Youssef
    Fouzia, Omary
    Powiązania:
    https://bibliotekanauki.pl/articles/2141815.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    cloud computing
    Big Data
    IoT
    recommender system
    KNN algorithm
    Opis:
    Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 65-75
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On explainable fuzzy recommenders and their performance evaluation
    Autorzy:
    Rutkowski, Tomasz
    Łapa, Krystian
    Nielek, Radosław
    Powiązania:
    https://bibliotekanauki.pl/articles/330650.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    recommender system
    explainable recommendation
    fuzzy system
    Akaike information criterion
    system rekomendacyjny
    system rozmyty
    kryterium informacyjne Akaike
    Opis:
    This paper presents a novel approach to the design of explainable recommender systems. It is based on the Wang–Mendel algorithm of fuzzy rule generation. A method for the learning and reduction of the fuzzy recommender is proposed along with feature encoding. Three criteria, including the Akaike information criterion, are used for evaluating an optimal balance between recommender accuracy and interpretability. Simulation results verify the effectiveness of the presented recommender system and illustrate its performance on the MovieLens 10M dataset.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 595-610
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative analysis of different trust metrics of user-user trust-based recommendation system
    Autorzy:
    Roy, Falguni
    Hasan, Mahamudul
    Powiązania:
    https://bibliotekanauki.pl/articles/27312906.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    trust-based recommender system
    Pearson correlation coefficient
    confidence
    mean absolute error
    precision
    recall
    coverage
    Opis:
    Information overload is the biggest challenge nowadays for any website – especially e-commerce websites. However, this challenge has arisen due to the fast growth of information on the web (WWW) along with easier access to the internet. A collaborative filtering-based recommender system is the most useful application for solving the information overload problem by filtering relevant information for users according to their interests. However, the current system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the above-mentioned issues, the relationship of trust incorporates in the system where it can be among users or items; such a system is known as a trust-based recommender system (TBRS). From the user perspective, the motive of a TBRS is to utilize the reliability among users to generate more-accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes 24 trust metrics in terms of the methodology, trust properties & measurements, validation approaches, and the experimented data set.
    Źródło:
    Computer Science; 2022, 23 (3); 335--373
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine Learning as a method of adapting offers to the clients
    Uczenie maszynowe jako metoda dostosowywania ofert do klientów
    Autorzy:
    Bielecki, Jacek
    Ceglarski, Oskar
    Skublewska-Paszkowska, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/98292.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Lubelska. Instytut Informatyki
    Tematy:
    recommender system
    collaborative filtering
    cognitive filtering
    machine learning
    system rekomendacji
    filtrowanie kolaboratywne
    filtrowanie kognitywne
    uczenie maszynowe
    Opis:
    Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.
    Systemy rekomendacji to aplikacje filtrujące dane, których głównym zadaniem jest dostarczanie spersonalizowanych rekomendacji produktów. Celem badań było dokonanie analizy i porównania dwóch metod uczenia maszynowego wykorzystywanych do generowania rekomendacji. System rekomendacji zbudowano na podstawie metody filtrowania kognitywnego opartej o treści oraz na podstawie metody filtrowania kolaboratywnego opartej o oceny użytkowników. Wnioski z przeprowadzonych badań pokazują wady i zalety obu metod.
    Źródło:
    Journal of Computer Sciences Institute; 2019, 13; 267-271
    2544-0764
    Pojawia się w:
    Journal of Computer Sciences Institute
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets
    Autorzy:
    Colomo-Palacios, R.
    González-Carrasco, I.
    López-Cuadrado, J. L.
    García-Crespo, Á.
    Powiązania:
    https://bibliotekanauki.pl/articles/331282.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    zbiór rozmyty
    zbiór przybliżony
    Scrum
    pakiet roboczy
    system polecenia
    fuzzy set
    rough set
    work package
    recommender system
    Opis:
    Agile development is a crucial issue within software engineering because one of the goals of any project leader is to increase the speed and flexibility in the development of new commercial products. In this sense, project managers must find the best resource configuration for each of the work packages necessary for the management of software development processes in order to keep the team motivated and committed to the project and to improve productivity and quality. This paper presents ReSySTER, a hybrid recommender system based on fuzzy logic, rough set theory and semantic technologies, aimed at helping project leaders to manage software development projects. The proposed system provides a powerful tool for project managers supporting the development process in Scrum environments and helping to form the most suitable team for different work packages. The system has been evaluated in a real scenario of development with the Scrum framework obtaining promising results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 801-816
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mining indirect association rules for web recommendation
    Autorzy:
    Kazienko, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/907860.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    reguły asocjacji
    system zalecany
    badanie sieci
    association rules
    indirect association rules
    recommender system
    web mining
    web usage mining
    Opis:
    Classical association rules, here called 'direct', reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, 'third' pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure-confidence-using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2009, 19, 1; 165-186
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    AI-supported reasoning in physiotherapy
    Wnioskowanie w fizjoterapii wspierane sztuczną inteligencją
    Autorzy:
    Mikołajewski, Dariusz
    Mikołajewska, Emilia
    Powiązania:
    https://bibliotekanauki.pl/articles/41203435.pdf
    Data publikacji:
    2024
    Wydawca:
    Uniwersytet Kazimierza Wielkiego w Bydgoszczy
    Tematy:
    artificial intelligence
    machine learning
    clinical reasoning
    clinical decision support system
    interview
    musculoskeletal pain disorders
    physiotherapy
    usability
    recommender system
    self-management
    mHealth
    sztuczna inteligencja
    uczenie maszynowe
    wnioskowanie kliniczne
    system wspomagania decyzji klinicznych
    wywiad
    zaburzenia bólowe układu mięśniowo-szkieletowego
    fizjoterapia
    użyteczność
    system rekomendacji
    samokontrola
    mZdrowie
    Opis:
    Artificial intelligence (AI)-based clinical reasoning support systems in physiotherapy, and in particular data-driven (machine learning) systems, can be useful in making and reviewing decisions regarding functional diagnosis and formulating/maintaining/modifying a rehabilitation programme. The aim of this article is to explore the extent to which the opportunities offered by AI-based systems for clinical reasoning in physiotherapy have been exploited and where the potential for their further stimulated development lies.
    Systemy wspomagania wnioskowania klinicznego w fizjoterapii oparte na sztucznej inteligencji, a w szczególności na danych (uczenie maszynowe), mogą być przydatne w podejmowaniu i weryfikacji decyzji dotyczących diagnostyki funkcjonalnej ora formułowania/utrzymywania/modyfikowania programu rehabilitacji. Celem niniejszego artykułu jest zbadanie, w jakim stopniu możliwości oferowane przez systemy oparte na sztucznej inteligencji w zakresie rozumowania klinicznego w fizjoterapii zostały wykorzystane i gdzie leży potencjał ich dalszego stymulowanego rozwoju.
    Źródło:
    Studia i Materiały Informatyki Stosowanej; 2024, 16, 2; 21-27
    1689-6300
    Pojawia się w:
    Studia i Materiały Informatyki Stosowanej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rozmyta mapa kognitywna jako inteligentny system rekomendacyjny zasobów strony internetowej
    Fuzzy cognitive map as an intelligent recommender system of website resources
    Autorzy:
    Jastriebow, A.
    Kubuś, Ł.
    Poczęta, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/408004.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    sztuczna inteligencja
    rozmyte mapy kognitywne
    system rekomendacyjny
    artificial intelligence
    fuzzy cognitive maps
    recommender systems
    Opis:
    Artykuł poświęcony jest budowie i analizie inteligentnego systemu rekomendacyjnego zasobów bazującego na rozmytej mapie kognitywnej. Opracowany system pozwala wskazać zasoby strony internetowej, którymi może być zainteresowany potencjalny użytkownik. Zasoby te są określane na podstawie aktywności innych użytkowników serwisu. Bazując na zbiorze anonimowo zebranych danych historycznych opracowano rozmytą mapę kognitywną, której czynniki odpowiadają poszczególnym zasobom strony internetowej. Wagi powiązań między nimi określono na podstawie liczby użytkowników odwiedzających poszczególne zasoby.
    This paper is devoted to the construction and analysis of the intelligent recommendation system for website resources based on fuzzy cognitive map. The developed system allows to identify resources, which may be interested in a potential user. These resources are determined on the basis of website users activity. Fuzzy cognitive map was develop using the dataset with anonymous collected historical data. The concepts of fuzzy cognitive map are identifiers of resources of website. Weights of the connection between them have been established based on the number of users visiting the resources.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2017, 7, 4; 74-78
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    E-tourism recommender systems: a survey and development perspectives
    Autorzy:
    Artemenko, O.
    Kunanets, O.
    Pasichnyk, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/410644.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    recommender systems
    mobile technologies
    e-tourism
    information technologies
    trip support
    system rekomendujacy
    technologie mobilne
    e-turystyka
    technologie informacyjne
    obsługa wycieczek
    Opis:
    This paper describes main modern tendencies of the design and development of intelligent information technologies, implementing process of generating recommendations in tourism recommender systems. The basic trends of the e-tourism information technology tools are analyzed to show importance of creation for multitask, multykontent mobile e-tourism recommender systems with decision support functions in terms of the tourism group, which is regarded as a single collective user.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 91-95
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-14 z 14

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