A HYBRID MOVIE RECOMMENDER SYSTEM BASED ON NEURAL NETWORKS

2007 ◽  
Vol 16 (05) ◽  
pp. 771-792 ◽  
Author(s):  
CHRISTINA CHRISTAKOU ◽  
SPYROS VRETTOS ◽  
ANDREAS STAFYLOPATIS
Keyword(s):  
Neural Networks ◽  
Hybrid System ◽  
Recommender System ◽  
Information Search ◽  
High Accuracy ◽  
Aggregation Operators ◽  
User Preferences ◽  
The Internet ◽  
Content Filtering ◽  
Fuzzy Aggregation

Recommender systems offer a solution to the problem of successful information search in the knowledge reservoirs of the Internet by providing individualized recommendations. Content-based and Collaborative Filtering are usually applied to predict recommendations. A combination of the results of the above techniques is used in this work to construct a system that provides precise recommendations concerning movies. The content filtering part of the system is based on trained neural networks representing individual user preferences. Filtering results are combined using Boolean and fuzzy aggregation operators. The proposed hybrid system was tested on the MovieLens data yielding high accuracy predictions.

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Executive Dysfunction Mediates the Association Between Older Age and Online Health Information Search Performance

2020 ◽  
Author(s):  
Victoria Kordovski ◽  
Savanna M. Tierney ◽  
Samina Rahman ◽  
Luis D. Medina ◽  
Michelle A. Babicz ◽  
...  
Keyword(s):  
Older Adults ◽  
Executive Functions ◽  
Information Search ◽  
Search Task ◽  
Executive Dysfunction ◽  
Older Age ◽  
The Internet ◽  
Younger Adults ◽  
Related Information ◽  
Health Related

Objective: Searching the Internet for health-related information is a complex and dynamic goal-oriented process that places demands on executive functions, which are higher-order cognitive abilities that are known to deteriorate with older age. This study aimed to examine the effects of older age on electronic health (eHealth) search behavior, and to determine whether executive functions played a mediating role in that regard. Method: Fifty younger adults (≤ 35 years) and 41 older adults (≥50 years) completed naturalistic eHealth search tasks involving fact-finding (Fact Search) and symptom diagnosis (Symptom Search), a neurocognitive battery, and a series of questionnaires. Results: Multiple regression models with relevant covariates revealed that older adults were slower and less accurate than younger adults on the eHealth Fact Search task, but not on the eHealth Symptom Search task. Nevertheless, executive functions mediated the relationship between older age and eHealth Fact Search and Symptom Search accuracy. Conclusions: Older adults can experience difficulty searching the Internet for some health-related information, which is at least partly attributable to executive dysfunction. Future studies are needed to determine the benefits of training in the organizational and strategic aspects of Internet search for older adults and whether these findings are applicable to clinical populations with executive dysfunction.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

NEURAL NETWORK METHOD OF ADAPTING THE PARAMETERS OF THE INFORMATION SYSTEMS INTERFACE

2020 ◽  
pp. 26-31
Author(s):  
А.Д. Обухов ◽  
М.Н. Краснянский ◽  
М.С. Николюкин
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Statistical Processing ◽  
Low Complexity ◽  
User Preferences ◽  
Expert Assessment ◽  
Practical Implementation ◽  
Algorithmic Support ◽  
Interface Parameters

Рассматривается проблема выбора оптимальных параметров интерфейса в информационных системах с целью его персонализации под предпочтения пользователя и возможности его оборудования. В настоящее время для ее решения используется алгоритмическое обеспечение и статистическая обработка предпочтений пользователей, что не обеспечивает достаточной гибкости и точности. Поэтому в данной работе предлагается применение разработанного метода адаптации параметров интерфейса, основанного на анализе и обработке пользовательской информации с помощью нейронных сетей. Научная новизна метода заключается в автоматизации сбора, анализа данных и настройки интерфейса за счет использования и интеграции нейронных сетей в информационную систему. Рассмотрена практическая реализация предлагаемого метода на Python. Экспертная оценка адаптивности интерфейса тестовой информационной системы после внедрения разработанного метода показала его перспективность и эффективность. Разработанный метод показывает лучшую точность и низкую сложность программной реализации относительно классического алгоритмического подхода. Полученные результаты могут использоваться для автоматизации процесса выбора компонентов интерфейса различных информационных систем. Дальнейшие исследования заключаются в развитии и интеграции разработанного метода в рамках фреймворка адаптации информационных систем Here we consider the problem of choosing the optimal parameters of the interface in information systems with the aim of personalizing it for the preferences of the user and the capabilities of his equipment. Currently, algorithmic support and statistical processing of user preferences are used to solve it, which does not provide sufficient flexibility and accuracy. Therefore, in this work, we propose the application of the developed method for adapting interface parameters based on the analysis and processing of user information using neural networks. The scientific novelty of the method is to automate the collection, analysis of data and interface settings through the use and integration of neural networks in the information system. We consider the practical implementation of the proposed method in Python. An expert assessment of the adaptability of the interface of the test information system after the implementation of the developed method showed its availability and efficiency. The developed method shows the best accuracy and low complexity of software implementation relative to the classical algorithmic approach. The results obtained can be used to automate the selection of interface components for various information systems. Further research consists in the development and integration of the developed method within the framework of the information systems adaptation framework

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

scholarly journals Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fu Jie Tey ◽  
Tin-Yu Wu ◽  
Chiao-Ling Lin ◽  
Jiann-Liang Chen
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Mobile Networks ◽  
Reference Value ◽  
User Preferences ◽  
Digital Information ◽  
Internet Applications ◽  
Social Media Networks ◽  
Recommendation Accuracy ◽  
Gain Access

AbstractRecent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

scholarly journals High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 48-56
Author(s):  
Max Pargmann ◽  
Daniel Maldonado Quinto ◽  
Peter Schwarzbözl ◽  
Robert Pitz-Paal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Data Driven ◽  
State Prediction ◽  
Accuracy Data
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