Temat: recommender system - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Analysis of content recommendation methods in information services
Analiza metod rekomendacji treści w serwisach informacyjnych
Autorzy:: Necheporuk, Oleksandr
Vashchenko, Svitlana
Fedotova, Nataliia
Baranova, Iryna
Dehtiarenko, Yaroslava
Tematy:: content-based recommender system
collaborative recommender system
hybrid recommender system
system rekomendacji oparty na treści
system rekomendacji oparty na współpracy
hybrydowy system rekomendacji; Pokaż więcej
Wydawca:: Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Powiązania:: https://bibliotekanauki.pl/articles/58907964.pdf Link otwiera się w nowym oknie
Opis:: The object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they operate on, thus prompting the utilisation of recommendation systems to maintain user engagement. Considering the different types of content, it is necessary to address the problem of data filtration based on their characteristics and user preferences. To solve this task, we analysed content-based and collaborative filtering methods using various techniques (model-based, memory-based, and hybrid collaborative filtering techniques), knowledge-based filtering, and hybrid filtering methods. Considering each method's advantages and disadvantages, we chose a hybrid method using model-based collaborative filtering and content-based filtering for the future development of our universal recommendation system.
Przedmiotem badań jest proces wyboru metody rekomendacji treści w serwisach informacyjnych. Trafność badania wynika z szybkiego rozwoju zasobów informacyjnych i rozrywkowych oraz wzrostu ilości danych, na których działają, dlatego w celu utrzymania uwagi użytkownika wykorzystywane są systemy rekomendacyjne. Biorąc pod uwagę różne rodzaje treści, konieczne jest rozwiązanie problemu filtrowania danych na podstawie ich charakterystyki i preferencji użytkownika. Aby rozwiązać problem, przeanalizowano metody filtrowania treści, filtrowania kooperacyjnego z wykorzystaniem różnych technik (technika oparta na modelu, technika oparta na pamięci i hybrydowa technika filtrowania kolaboracyjnego), filtrowanie oparte na wiedzy oraz metody filtrowania hybrydowego. Biorąc pod uwagę zalety i wady każdej metody, wybrano metodę hybrydową wykorzystującą filtrowanie kolaboracyjne oparte na modelu i filtrowanie oparte na treści do przyszłego rozwoju proponowanego uniwersalnego systemu rekomendacji.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: The recommendation algorithm for an online art gallery
Autorzy:: Karwowski, W.
Sosnowska, J.
Rusek, M.
Tematy:: algorithms
recommender system
collaborative filtering; Pokaż więcej
Wydawca:: Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Powiązania:: https://bibliotekanauki.pl/articles/94759.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A new approach to image-based recommender systems with the application of heatmaps maps
Autorzy:: Woldan, Piotr
Duda, Piotr
Cader, Andrzej
Laktionov, Ivan
Tematy:: feature extraction
recommender system
heatmap; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/2201330.pdf Link otwiera się w nowym oknie
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
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms
Autorzy:: Pawłowska, Justyna
Rydzewska, Klara
Wierzbicki, Adam
Tematy:: recommender system
cognitive limitations
aging
e-commerce; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/2201326.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Recommender system for navigation safety: requirements and methodology
Autorzy:: Shilov, N.
Tematy:: navigational safety
navigation safety
methodology
recommender system
maneuverability
recommender system for navigation safety
advanced driver assistance systems
Safety at Sea; Pokaż więcej
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Powiązania:: https://bibliotekanauki.pl/articles/117388.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Application of multi-criteria analysis based on individual psychological profile for recommender systems
Autorzy:: Rafalak, M.
Granat, J.
Wierzbicki, A. P.
Tematy:: recommender system
multi-criteria analysis
user profiling; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/305393.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems
Autorzy:: Indurkhya, Bipin
Misztal-Radecka, Joanna
Opis:: One challenge for the modern recommendation systems is the Tyranny of Majority - the generated recommendations are often optimized for the mainstream trends so that the minority preference groups remain discriminated. Moreover, most modern recommendation techniques are characterized as black-box systems. Given a lack of understanding of the dataset characteristics and insufficient diversity of represented individuals, such approaches inevitably lead to amplifying hidden data biases and existing disparities. In this research, we address this problem by proposing a novel approach to detecting and describing potentially discriminated user groups for a given recommendation algorithm. We propose a Bias-Aware Hierarchical Clustering algorithm that identifies user clusters based on latent embeddings constructed by a black-box recommender to identify users whose needs are not met by the given recommendation method. Next, a post-hoc explainer model is applied to reveal the most important descriptive features that characterize these user segments. Our method is model-agnostic and does not require any a priori information about existing disparities and sensitive attributes. An experimental evaluation on a synthetic dataset and two real-world datasets from different domains shows that, compared with other clustering methods and arbitrarily selected user groups, our method is capable of identifying underperforming segments for different recommendation algorithms, and detect more severe disparities.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 8.

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
Tematy:: cloud computing
Big Data
IoT
recommender system
KNN algorithm; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/2141815.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Meta‐User2Vec model for addressing the user and item cold‐start problem in recommender systems
Autorzy:: Indurkhya, Bipin
Smywiński‐Pohl, Aleksander
Misztal‐Radecka, Joanna
Opis:: The cold-start scenario is a critical problem for recommendation systems, especially in dynamically changing domains such as online news services. In this research, we aim at addressing the cold-start situation by adapting an unsupervised neural User2Vec method to represent new users and articles in a multidimensional space. Toward this goal, we propose an extension of the Doc2Vec model that is capable of representing users with unknown history by building embeddings of their metadata labels along with item representations. We evaluate our proposed approach with respect to different parameter configurations on three real-world recommendation datasets with different characteristics. Our results show that this approach may be applied as an efficient alternative to the factorization machine-based method when the user and item metadata are used and hence can be applied in the cold-start scenario for both new users and new items. Additionally, as our solution represents the user and item labels in the same vector space, we can analyze the spatial relations among these labels to reveal latent interest features of the audience groups as well as possible data biases and disparities.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 10.

Tytuł:: Comparative analysis of different trust metrics of user-user trust-based recommendation system
Autorzy:: Roy, Falguni
Hasan, Mahamudul
Tematy:: trust-based recommender system
Pearson correlation coefficient
confidence
mean absolute error
precision
recall
coverage; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/27312906.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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