scholarly journals Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lili Wu
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Laplacian Matrix ◽  
Network Information ◽  
Recommendation Algorithms ◽  
Text Word ◽  
Scoring Matrix ◽  
Accuracy Of Prediction ◽  
The Impact ◽  
Relational Network

In view of the poor recommendation performance of traditional resource collaborative filtering recommendation algorithms, this article proposes a collaborative filtering recommendation model based on deep learning for art and MOOC resources. This model first uses embedding vectors based on the context of metapaths for learning. Embedding vectors based on the context of metapaths aggregate different metapath information and different MOOCs may have different preferences for different metapaths. Secondly, to capture this preference drift, the model introduces an attention mechanism, which can improve the interpretability of the recommendation results. Then, by introducing the Laplacian matrix into the prior distribution of the hidden factor feature matrix, the relational network information is effectively integrated into the model. Finally, compared with the traditional model using the scoring matrix, the model in this article using text word vectors effectively alleviates the impact of data sparsity and greatly improves the accuracy of prediction. After analyzing the experimental results, compared with other algorithms, the resource collaborative filtering recommendation model proposed in this article has achieved better recommendation results, with good stability and scalability.

scholarly journals Attention neural collaboration filtering based on GRU for recommender systems

2021 ◽  
Author(s):  
Hongbin Xia ◽  
Yang Luo ◽  
Yuan Liu
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Auxiliary Information ◽  
User Preferences ◽  
Feature Representation ◽  
Long Distance ◽  
Data Set ◽  
Filtering Method ◽  
The Impact

AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

A restaurant recommendation approach with the contextual information

2021 ◽  
pp. 1-9
Author(s):  
Lei Chen ◽  
Meimei Xia
Keyword(s):  
Collaborative Filtering ◽  
Contextual Information ◽  
Current Time ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Inherent Problem ◽  
Scoring Matrix ◽  
Time Price ◽  
Potential Customers

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones.

scholarly journals Agricultural Product Recommendation Model based on BMF

2020 ◽  
Vol 5 (2) ◽  
pp. 415-424
Author(s):  
Fucheng Wan ◽  
Dengyun Zhu ◽  
Xiangzhen He ◽  
Qi Guo ◽  
Dongjiao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Language Model ◽  
Agricultural Product ◽  
Vector Construction ◽  
Proposed Model ◽  
Memory Network ◽  
Scoring Matrix ◽  
Short Time ◽  
Matrix Factorisation

AbstractIn this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.

scholarly journals Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tongyan Li ◽  
Yingxiang Li ◽  
Chen Yi-Ping Phoebe
Keyword(s):  
Collaborative Filtering ◽  
Sparse Data ◽  
Current Data ◽  
Prediction Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Scoring Matrix ◽  
Rating Prediction ◽  
User Ratings

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

Hospital Informatization in China During the COVID-19 Pandemic: A Cross-Sectional Internet-Based Survey Study (Preprint)

2020 ◽  
Author(s):  
Ke Zeng ◽  
Weiguo Zhu ◽  
Caiyou Wang ◽  
Liyan Zhu
Keyword(s):  
Information Technology ◽  
Information Security ◽  
Medical Information ◽  
Survey Study ◽  
Chinese Government ◽  
Qr Code ◽  
Cross Sectional ◽  
Network Information ◽  
Tertiary Hospitals ◽  
The Impact

BACKGROUND The rapid spread of COVID-19 has created a severe challenge to China’s healthcare system. Hospitals across the country reacted quickly under the leadership of the Chinese government and implemented a range of informatization measures to effectively respond to the COVID-19. OBJECTIVE To understand the impact of the pandemic on the medical business of Chinese hospitals and the difficulties faced by hospital informatization construction. To discuss the application of hospital informatization measures during the COVID-19 pandemic. To summarize the practical experience of hospitals using information technology to fight the pandemic. METHODS Performing a cross-sectional on-line questionnaire survey in Chinese hospitals, of which the participants are invited including hospital information staff, hospital administrators, medical staff, etc. Statistical analyzing the collected data by using SPSS version 24. RESULTS A total of 804 valid questionnaires (88.45%) are collected in this study from 30 provinces in mainland China, of which 731 (90.92%) were filled out by hospital information staff. 473 (58.83%) hospitals are tertiary hospitals while the remaining 331 (41.17%) are secondary hospitals. The majority hospitals (82.46%) had a drop in their business volume during the pandemic and a more substantial drop is found in tertiary hospitals. 70.40% (n=566) of hospitals have upgraded or modified their information systems in response to the epidemic. The proportion of tertiary hospitals that upgraded or modified systems is significantly higher than that of secondary hospitals. Internet hospital consultation (70.52%), pre-check and triage (62.56%), telemedicine (60.32%), health QR code (57.71%), and telecommuting (50.87%) are the most used informatization anti-pandemic measures. There are obvious differences in the application of information measures between tertiary hospitals and secondary hospitals. Among these measures, most of them (41.17%) are aiming at serving patients and most of them (62.38%) are universal which continue to be used after pandemic. The informatization measures are mostly used to control the source of infection (48.19%), such as health QR Code, etc. During the pandemic, the main difficulties faced by the hospital information department are “information construction projects are hindered” (58.96%) and “increased difficulty in ensuring network information security” (58.58%). There are significant differences in this issue between tertiary hospitals and secondary hospitals. The shortcomings of hospital informatization that should be made up for are “shorten patient consultation time and optimize consultation process” (72.51%), “Ensure network information security” (72.14%) and “build internet hospital consultations platform” (59.95%). CONCLUSIONS A significant number of innovative medical information technology have been used and played a significant role in all phases of COVID-19 prevention and control in China. Since the COVID-19 brought many challenges and difficulties for informatization work, hospitals need to constantly improve their own information technology skills to respond to public health emergencies that arise at any moment.

scholarly journals The impact of deep learning on document classification using semantically rich representations

2019 ◽  
Vol 56 (5) ◽  
pp. 1618-1632 ◽  
Author(s):  
Zenun Kastrati ◽  
Ali Shariq Imran ◽  
Sule Yildirim Yayilgan
Keyword(s):  
Deep Learning ◽  
Document Classification ◽  
The Impact

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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