Hybrid recommendation model based on deep learning and Stacking integration strategy

2020 ◽  
Vol 24 (6) ◽  
pp. 1329-1344
Author(s):  
Xiaolan Xie ◽  
Shantian Pang ◽  
Jili Chen
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Structural Features ◽  
Internet Technology ◽  
Inner Product ◽  
Learning Technology ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Integration Strategy ◽  
Hybrid Recommendation

In the traditional recommendation algorithms, due to the rapid development of deep learning and Internet technology, user-item rating data is becoming increasingly sparse. The simple inner product interaction mode adopted by the collaborative filtering method has a cold start problem and cannot learn the complex nonlinear structural features between users and items, while the content-based algorithm encounters the difficulty of effective feature extraction. In response to this problem, a hybrid model is proposed based on deep learning and Stacking integration strategy. The traditional recommendation algorithm is first fused by using the Stacking integration strategy to make up for the shortcomings of the single recommendation algorithm to achieve better recommendation performance. The fusion-based model learns the more abstract and deeper nonlinear interaction features by deep learning technology, which makes the model performance gain further. The experiment comparison on the MovieLens-1m dataset shows that the proposed hybrid recommendation model can significantly improve the accuracy of rating prediction.

Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Gao ◽  
Haiming Liang ◽  
Bingzhen Sun
Keyword(s):  
Online Shopping ◽  
Rapid Development ◽  
Dynamic Network ◽  
Topsis Method ◽  
Recommendation Algorithm ◽  
Time Periods ◽  
The Difference ◽  
Relationship Of ◽  
Hybrid Recommendation ◽  
Novel Model

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.

Super-Resolution Based on Generative Adversarial Network for HRTEM Images

2021 ◽  
pp. 2154027
Author(s):  
Fuqi Mao ◽  
Xiaohan Guan ◽  
Ruoyu Wang ◽  
Wen Yue
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Information Structure ◽  
Rapid Development ◽  
Super Resolution ◽  
Training Data ◽  
Learning Technology ◽  
Hrtem Image ◽  
Generative Adversarial Network ◽  
Dual Regression

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals Research on Students’ Adaptive Learning System Based on Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xi Yang ◽  
Zhihan Zhou ◽  
Yu Xiao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Rapid Development ◽  
Learning Model ◽  
Learning System ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Adaptive Learning System ◽  
Deep Learning Model

With the rapid development of deep learning in recent years, recommendation algorithm combined with deep learning model has become an important direction in the field of recommendation in the future. Personalized learning resource recommendation is the main way to realize students’ adaptation to the learning system. Based on the in-depth learning mode, students’ online learning action data are obtained, and further learning analysis technology is used to construct students’ special learning mode and provide suitable learning resources. The traditional method of introducing learning resources mainly stays at the level of examination questions. What ignores the essence of students’ learning is the learning of knowledge points. Students’ learning process is affected by “before” and “after” learning behavior, which has the characteristics of time. Among them, bidirectional length cyclic neural network is good at considering the “front” and “back” states of recommended nodes when recommending prediction results. For the above situation, this paper proposes a recommendation method of students’ learning resources based on bidirectional long-term and short-term memory cyclic neural network. Firstly, recommend the second examination according to the knowledge points, predict the scores of important steps including the accuracy of the recommended examination of the target students and the knowledge points of the recommended examination, and finally cooperate with the above two prediction results to judge whether the examination questions are finally recommended. Through the comparative experiment with the traditional recommendation algorithm, it is found that the student adaptive learning system based on the deep learning model proposed in this paper has better stability and interpretability in the recommendation results.

scholarly journals Analysis of Physical Expansion Training Based on Edge Computing and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhongle Liu
Keyword(s):  
Artificial Intelligence ◽  
Resource Allocation ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Rapid Development ◽  
Computing Time ◽  
Edge Computing ◽  
Learning Technology ◽  
Effective Development ◽  
Physical Expansion

The effective development of physical expansion training benefits from the rapid development of computer technology, especially the integration of Edge Computing (EC) and Artificial Intelligence (AI) technology. Physical expansion training is mainly based on the collective form, and how to improve the quality of training to achieve results has become the content of everyone’s attention. As a representative technology in the field of AI, deep learning and EC evolving from traditional cloud computing technology are all well applied to physical expansion training. Traditional EC methods have problems such as high computing cost and long computing time. In this paper, deep learning technology is introduced to optimize EC methods. The EC cycle is set through the Internet of Things (IoT) topology to obtain the data upload speed. The CNN (Convolutional Neural Network) model introduces deep reinforcement learning technology, implements convolution calculations, and completes the resource allocation of EC for each trainer’s wearable sensor device, which realizes the optimization of EC based on deep reinforcement learning. The experiment results show that the proposed method can effectively control the server’s occupancy time, the energy cost of the edge server, and the computing cost. The proposed method in this paper can also improve the resource allocation ability of EC, ensure the uniform speed of the computing process, and improve the efficiency of EC.

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

2017 ◽  
Vol 14 (2) ◽  
pp. 45-66 ◽  
Author(s):  
Mingjun Xin ◽  
Yanhui Zhang ◽  
Shunxiang Li ◽  
Liyuan Zhou ◽  
Weimin Li
Keyword(s):  
User Behavior ◽  
Rapid Development ◽  
Mobile Internet ◽  
Location Based Services ◽  
Internet Technology ◽  
Behavior Prediction ◽  
Recommendation Algorithm ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Pattern Similarity

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

APPLICATION OF DEEP LEARNING TECHNOLOGY IN DISEASE DIAGNOSIS

2020 ◽  
Vol 04 (05) ◽  
pp. 4-11
Author(s):  
Elcin Nizami Huseyn ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rapid Development ◽  
Network Models ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Neural Network Models ◽  
Restricted Boltzmann Machines ◽  
New Methods ◽  
Development Direction

The rapid development of deep learning technology provides new methods and ideas for assisting physicians in high-precision disease diagnosis. This article reviews the principles and features of deep learning models commonly used in medical disease diagnosis, namely convolutional neural networks, deep belief networks, restricted Boltzmann machines, and recurrent neural network models. Based on several typical diseases, the application of deep learning technology in the field of disease diagnosis is introduced; finally, the future development direction is proposed based on the limitations of current deep learning technology in disease diagnosis. Keywords: Artificial Intelligence; Deep Learning; Disease Diagnosis; Neural Network

scholarly journals Design and Implementation of Intelligent Sports Training System for College Students' Mental Health Education

2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Wang ◽  
Jinkyung Park
Keyword(s):  
Artificial Intelligence ◽  
College Students ◽  
Deep Learning ◽  
Management System ◽  
Course Evaluation ◽  
Learning Technology ◽  
Sports Management ◽  
Sports Training ◽  
Recommendation Algorithm ◽  
Registration System

In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index (BMI), and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship (E-R) diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education.

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