scholarly journals Multi-Criteria Recommendation Systems to Foster Online Grocery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3747
Author(s):  
Manar Mohamed Hafez ◽  
Rebeca P. Díaz Redondo ◽  
Ana Fernández Vilas ◽  
Héctor Olivera Pazó
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Technology Development ◽  
Intelligent System ◽  
Recommendation Systems ◽  
Grocery Shopping ◽  
Document Representation ◽  
Specific Product ◽  
The Neural Network ◽  
Representation Method

With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system (RS) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. RS also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenge when recommending products is insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package and health) for each document representation method to foster online grocery shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Research on Intelligent Forecasting of Financial Market Parameter Time Series Based on Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 4596-4607
Author(s):  
Enyang Zhu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Financial Markets ◽  
Intelligent System ◽  
System Modeling ◽  
Layer By Layer ◽  
Healthy Development ◽  
The Neural Network ◽  
Deep Integration

Objectives: Deep learning has become the most representative and potential intelligent system modeling technology in artificial intelligence. However, the complexity of financial markets goes far beyond all economic games. Methods: This paper is devoted to the feasibility and efficiency of the deep-integration neural network model as one of the main paradigms of in-depth learning in the intelligent prediction of financial time. A prediction model of stack self-coding neural network composed of bottom stack self-coding and top regression neurons is proposed. Results: Firstly, the self-encoder unsupervised learning mechanism is used to identify and learn the time series, and the layers of the neural network are learned greedy layer by layer. Then the stack self-encoder is extended to the SAEP model with supervised mechanism, and the parameters learned by SAE are used. Used to initialize the neural network, and finally use the supervised learning to fine-tune the weights. Conclusion: The research results show that the model provides effective financial planning and decision-making basis for financial forecasting, maintains the healthy development of financial markets, and maximizes the benefits of profit-making institutions.

scholarly journals Recommendation Systems in the Big Data Era

2020 ◽  
Vol 8 (12s3) ◽  
pp. 80-85
Keyword(s):  
Social Media ◽  
Big Data ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Rapid Progression ◽  
Specific Product ◽  
Hybrid Approaches ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
The Way

Rapid progression in technology and increasing use of social media platforms like Facebook, Instagram and Twitter has altered the way of articulating people’s judgment, observation and sentiments about specific product, services, and more. This leads to the production and accumulation of massive amount of data. Recommendation systems are getting impetus when it comes to find insights from this data to make decisions that can be represented in various statistical and graphical forms. They have proven useful in predicting or recommending products ranging from food, movies, restaurants etc. This paper presents an overview about recommendation systems and a review of generation of recommendation methods based on categories like contentbased, collaborative, and hybrid approaches. The paper will enlist the limitations which the present recommendation system faces and the possible improvements required in their capabilities to fit into a wider range of application areas.

scholarly journals Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process

2022 ◽  
Vol 3 (4) ◽  
pp. 272-282
Author(s):  
Haoxiang Wang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Architectural Design ◽  
Recommendation System ◽  
Numerical Data ◽  
Fine Tuning ◽  
Classification Rate ◽  
The Neural Network ◽  
Hybrid Data ◽  
Neural Network Algorithm

Hybrid data mining processes are employed in recent days on several applications to achieve a better prediction and classification rate along with customer satisfaction. Hybrid data mining processes are the combination of different form of data considered for a neural network decision. In some cases, the different form of data represents image along with numerical data. In the proposed work, a food recommendation system is developed with respect to the flavour taste of the customer and considering the review comments of previous customers. The suggestions given by the users are taken into account as a feedback layer in the neural network for fine tuning the accuracy of the prediction process. The architectural design of the proposed model is employed with an ADNet (Adaptively Dense Convolutional Neural Network) algorithm to enable the usage of low range features in an efficient way. To verify the performance of the developed model, a pizza flavour recommender dataset is employed in the work for analysis. The experimental work analysis indicates that the ADNet algorithm works in a better way on a hybrid data analysis than the traditional DenseNet and ResNet algorithms.

scholarly journals Neural network control of a wheeled mobile robot based on optimal trajectories

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091607
Author(s):  
Pavol Bozek ◽  
Yury L Karavaev ◽  
Andrey A Ardentov ◽  
Kirill S Yefremov
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Intelligent System ◽  
Equations Of Motion ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
Euler’S Elastica ◽  
Wheeled Robot ◽  
Artificial Neural

This article is concerned with developing an intelligent system for the control of a wheeled robot. An algorithm for training an artificial neural network for path planning is proposed. The trajectory ensures steering optimal motion from the current position of the mobile robot to a prescribed position taking its orientation into account. The proposed control system consists of two artificial neural networks. One of them serves to specify the position and the size of the obstacle, and the other forms a continuous trajectory to reach it, taking into account the information received, the coordinates, and the orientation at the point of destination. The neural network is trained on the basis of samples obtained by modeling the equations of motion of the wheeled robot which ensure its motion along trajectories in the form of Euler’s elastica.

scholarly journals Automatic detection of thermal damage in grinding process by artificial neural network

2003 ◽  
Vol 56 (4) ◽  
pp. 295-300 ◽  
Author(s):  
Fábio Romano Lofrano Dotto ◽  
Paulo Roberto de Aguiar ◽  
Eduardo Carlos Bianchi ◽  
Rogério Andrade Flauzino ◽  
Gustavo de Oliveira Castelhano ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Damage ◽  
Intelligent System ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Output Variable ◽  
The Neural Network ◽  
Wheel Drive ◽  
Drive Motor ◽  
Grinding Conditions

This work aims to develop an intelligent system for detecting the workpiece burn in the surface grinding process by utilizing a multi-perceptron neural network trained to generalize the process and, in turn, obtnaing the burning threshold. In general, the burning occurrence in grinding process can be detected by the DPO and FKS parameters. However, these ones were not efficient at the grinding conditions used in this work. Acoustic emission and electric power of the grinding wheel drive motor are the input variable and the output variable is the burning occurrence to the neural network. In the experimental work was employed one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model ART 3TG80.3 NVHB.

scholarly journals News recommendation system using machine learning

2018 ◽  
Vol 7 (2.18) ◽  
pp. 32
Author(s):  
Neha Rani ◽  
Sudhir Sudhir Pathak
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
High Accuracy ◽  
Svm Classifier ◽  
Financial News ◽  
The Neural Network ◽  
Market Forecast ◽  
News Recommendation ◽  
Weighted Values ◽  
The Given

The forecasting of financial news is yet becoming the main issue to divide the new into different classes on the basis of present time series. Moreover, it might be utilized for predicting and analyzing the stock market for the particular industry. Thus, the new content is significantly important to influence market forecast report. In this paper, the financial news from four countries namely America, Australia, India and South Africa along with their stop words are consider. The words along with their weighted values are determined and then the neural network is trained. Here, artificial neural network is used for classifying the appropriate results for the given input data. At last the comparison of ANN with SVM is shown. Experiments show that the ANN classification provides high accuracy to predict the news than the SVM classifier. 

scholarly journals Detection of Breast Cancer by Thermal Based Sensors using Multilayered Neural Network Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 5615-5618
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Intelligent System ◽  
Early Stage ◽  
Imaging Modality ◽  
Public Health Problem ◽  
Negative Temperature ◽  
X Rays ◽  
Correct Treatment ◽  
The Neural Network

Consideration of public health problem issues, one of the most common diseases in public is cancer. Most of the women population is suffering from breast cancer which is the most well known appearance of cancer in metropolitan cities of India and abroad. There many number of imaging modalities to diagnose cancerous cells. Among those, mammography is alone an imaging modality which diagnoses the breast cancer at an early stage. Furthermore, this modality involves X-rays which are more harmful to human health and make the patient inconvenience. Through the mammogram, doctors can analyze, estimate and evaluate the cancer stage so that doctors can give better and correct treatment to the patients. With this mortality and death rates can also be diminished up to some extent. In this paper, the author proposed an intelligent system to identify and find out the severity of breast cancer. By using a thermal based sensor which is of negative Temperature Coefficient (NTC) available with C-MET Thrissur which replaces Mammography. The stage at which the cancer is progressing is classified with the help of Intelligent System Algorithms which works on the temperature data obtained from the thermal device. The data is pre-processed and applied to multilayered backpropogation neural network model. The neural network classifies the preprocessed images into normal, benign and cancer. The output of the network is presented to the doctors through graphs and displays.

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