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.

Novel Application to Recognize a Breakdown Pressure Event on Time Series Frac Data Vs. an Artificial Intelligence Approach

2021 ◽  
Author(s):  
Alberto Jose Ramirez ◽  
Jessica Graciela Iriarte
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Service Providers ◽  
Complex Model ◽  
Rate Of Change ◽  
Computational Time ◽  
Breakdown Pressure ◽  
Constant Multiple ◽  
Artificial Neural ◽  
Rule Based Approach

Abstract Breakdown pressure is the peak pressure attained when fluid is injected into a borehole until fracturing occurs. Hydraulic fracturing operations are conducted above the breakdown pressure, at which the rock formation fractures and allows fluids to flow inside. This value is essential to obtain formation stress measurements. The objective of this study is to automate the selection of breakdown pressure flags on time series fracture data using a novel algorithm in lieu of an artificial neural network. This study is based on high-frequency treatment data collected from a cloud-based software. The comma separated (.csv) files include treating pressure (TP), slurry rate (SR), and bottomhole proppant concentration (BHPC) with defined start and end time flags. Using feature engineering, the model calculates the rate of change of treating pressure (dtp_1st) slurry rate (dsr_1st), and bottomhole proppant concentration (dbhpc_1st). An algorithm isolates the initial area of the treatment plot before proppant reaches the perforations, the slurry rate is constant, and the pressure increases. The first approach uses a neural network trained with 872 stages to isolate the breakdown pressure area. The expert rule-based approach finds the highest pressure spikes where SR is constant. Then, a refining function finds the maximum treating pressure value and returns its job time as the predicted breakdown pressure flag. Due to the complexity of unconventional reservoirs, the treatment plots may show pressure changes while the slurry rate is constant multiple times during the same stage. The diverse behavior of the breakdown pressure inhibits an artificial neural network's ability to find one "consistent pattern" across the stage. The multiple patterns found through the stage makes it difficult to select an area to find the breakdown pressure value. Testing this complex model worked moderately well, but it made the computational time too high for deployment. On the other hand, the automation algorithm uses rules to find the breakdown pressure value with its location within the stage. The breakdown flag model was validated with 102 stages and tested with 775 stages, returning the location and values corresponding to the highest pressure point. Results show that 86% of the predicted breakdown pressures are within 65 psi of manually picked values. Breakdown pressure recognition automation is important because it saves time and allows engineers to focus on analytical tasks instead of repetitive data-structuring tasks. Automating this process brings consistency to the data across service providers and basins. In some cases, due to its ability to zoom-in, the algorithm recognized breakdown pressures with higher accuracy than subject matter experts. Comparing the results from two different approaches allowed us to conclude that similar or better results with lower running times can be achieved without using complex algorithms.

scholarly journals Robotic grasp detection using a novel two-stage approach

2021 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Zhe Chu ◽  
Mengkai Hu ◽  
Xiangyu Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Particle Swarm Optimizer ◽  
Neural Network Models ◽  
Two Stage ◽  
The Neural Network ◽  
End To End ◽  
Small Change ◽  
Robotic Grasp

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

scholarly journals AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2132
Author(s):  
Xintao Ma ◽  
Liyan Dong ◽  
Yuequn Wang ◽  
Yongli Li ◽  
Minghui Sun
Keyword(s):  
Recommendation System ◽  
Bipartite Graphs ◽  
Research Topic ◽  
Online Information ◽  
Unique Type ◽  
Implicit Relation ◽  
Recommendation Algorithms ◽  
Relation Graph ◽  
Content Information ◽  
Better Than

With users being exposed to the growing volume of online information, the recommendation system aiming at mining the important or interesting information is becoming a modern research topic. One approach of recommendation is to integrate the graph neural network with deep learning algorithms. However, some of them are not tailored for bipartite graphs, which is a unique type of heterogeneous graph having two entity types. Others, though customized, neglect the importance of implicit relation and content information. In this paper, we propose the attentive implicit relation recommendation incorporating content information (AIRC) framework that is designed for bipartite graphs based on the GC–MC algorithm. First, through reconstructing the bipartite graphs, we obtain the implicit relation graphs. Then we analyze the content information of users and items with a CNN process, so that each user and item has its feature-tailored embeddings. Besides, we expand the GC–MC algorithms by adding a graph attention mechanism layer, which handles the implicit relation graph by highlighting important features and neighbors. Therefore, our framework takes into consideration both the implicit relation and content information. Finally, we test our framework on Movielens dataset and the results show that our framework performs better than other state-of-art recommendation algorithms.

MPID Control Tuning for a Flexible Manipulator Using a Neural Network

2010 ◽  
Vol 22 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Tamer Mansour ◽  
◽  
Atsushi Konno ◽  
Masaru Uchiyama
Keyword(s):  
Neural Network ◽  
Vibration Control ◽  
Learning Algorithm ◽  
The Other ◽  
Gradient Algorithm ◽  
Flexible Manipulator ◽  
Computational Time ◽  
Speed Of Response ◽  
Control Gain ◽  
The Neural Network

This paper studies the use of neural networks as a tuning tool for the gain in Modified Proportional-Integral-Derivative (MPID) control used to control a flexible manipulator. The vibration control gain in the MPID controller has been determined in an empirical way so far. It is a considerable time consuming process because the vibration control performance depends not only on the vibration control gain but also on the other parameters such as the payload, references and PD joint servo gains. Hence, the vibration control gain must be tuned considering the other parameters. In order to find optimal vibration control gain for the MPID controller, a neural network based approach is proposed in this paper. The proposed neural network finds an optimum vibration control gain that minimizes a criteria function. The criteria function is selected to represent the effect of the vibration of the end effector in addition to the speed of response. The scaled conjugate gradient algorithm is used as a learning algorithm for the neural network. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the neural network appears in the reduction of the computational time and the ability to tune the gain with different loading condition.

scholarly journals Research and Analysis of Recommendation Algorithm Based on Convolutional Neural Network

2021 ◽  
Vol 2132 (1) ◽  
pp. 012011
Author(s):  
Yuhang Ding ◽  
Wenrong Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Development Trend ◽  
Research Progress ◽  
Data Types ◽  
Research Directions ◽  
Continuous Growth ◽  
Main Research ◽  
Recommendation Algorithms

Abstract This paper first introduces the situation that the traditional recommendation algorithms cannot meet the needs of users for accurate and efficient recommendation results because of the continuous growth of data and the increasingly diversified data types. Then it introduces the Deep Learning (DL) algorithm which is getting more and more attention, organizes the research progress of recommendation system based on Convolutional Neural Networks (CNN) in recent years, analyzes its advantages compared with traditional recommendation algorithms. The main research directions and application progress are classified, compared and summarized. Finally, the future development trend of recommendation system based on convolutional neural network is summarized and analyzed.

scholarly journals Improving flood forecasting in Bangladesh using an artificial neural network

2009 ◽  
Vol 12 (3) ◽  
pp. 351-364 ◽  
Author(s):  
A. S. Islam
Keyword(s):  
Neural Network ◽  
Absolute Error ◽  
Flood Forecasting ◽  
Computational Time ◽  
Output Node ◽  
River Stage ◽  
The Neural Network ◽  
Field Data Collection ◽  
The Ganges ◽  
Very High

A river stage neural network model has been developed to study and predict the water level of Dhaka city. A total of five stations located at the border area of Bangladesh on the Ganges, Brahmaputra and Meghna rivers are selected as input nodes and Dhaka on the Buriganga river is the output node for the neural network. This model is trained with river stage data for a period of 1998 to 2004 and validated with data from 2005 to 2007. The river stage of Dhaka has been predicted for up to ten days with very high accuracy. Values of R2, root mean square and mean absolute error are found ranging from 0.537 to 0.968, 0.607 m to 0.206 m and 0.475 m to 0.154 m, respectively, during training and validation of the model. The results of this study can be useful for real-time flood forecasting by reducing computational time, improving water resources management and reducing the unnecessary cost of field data collection.

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

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.

A Neural Network-Based Prediction of the Weld Lines in Resin Transfer Molded Parts

1999 ◽  
Author(s):  
Faezeh Soltani ◽  
Souran Manoochehri
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Simulation Models ◽  
Optimization Methods ◽  
Computational Time ◽  
Back Propagation Algorithm ◽  
Rtm Process ◽  
The Neural Network ◽  
Molded Parts ◽  
The Neural Networks

Abstract A model is developed to predict the weld lines in Resin Transfer Molding (RTM) process. In this model, the preforms are assumed to be thin flat with isotropic and orthotropic permeabilities. The position of the weld lines formed by multiple specified inlet ports are predicted using a neural network-based back propagation algorithm. The neural network was trained with data obtained from simulation and actual molding experimentation. Part geometry is decomposed into smaller sections based on the position of the weld lines. The variety of preforms and processing conditions are used to verify the model. Applying the neural networks reduced the amount of computational time by several orders of magnitude compared with simulations. The models developed in this study can be effectively utilized in iterative optimization methods where use of numerical simulation models is cumbersome.

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