A Study of Deep Learning-based Personalized Recommendation Service for Solving Online Hotel Review and Rating Mismatch Problem

2021 ◽  
Vol 23 (3) ◽  
pp. 51-75
Author(s):  
Qinglong Li ◽  
◽  
Shibo Cui ◽  
Byunggyu Shin ◽  
Jaekyeong Kim
Keyword(s):  
Deep Learning ◽  
Personalized Recommendation ◽  
Mismatch Problem

scholarly journals Design of Personalized Recommendation System for Swimming Teaching Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhan Shi ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
The Body ◽  
Test Method ◽  
Personalized Recommendation ◽  
Survey Method ◽  
Sporting Event ◽  
Motivation For Learning ◽  
Great Confidence ◽  
The Right

Swimming is not only an entertaining hobby but also a sporting event. It is a sport for strengthening the body. Although there are many swimming coaches, there are different swimming teaching courses. However, choosing the right swimming instructor or course is the motivation for learning swimming activities. To this end, this paper conducts related research on the personalized recommendation system for swimming teaching based on deep learning with the purpose of improving the accuracy of the recommendation system to meet the needs of the users and promote the development of swimming events. This article mainly uses the experimental test method, the system construction method, and the questionnaire survey method to analyze and study the personalized swimming teaching system and the students’ attitude to it and draw a conclusion finally. The data results show that the accuracy of the system designed in this paper can meet the basic requirements. Hence, it can bring an excellent experience to the users. According to the questionnaire data, 85%–95% of people have great confidence in the personalized recommendation system.

scholarly journals Personalized Movie Recommendation Method Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jingdong Liu ◽  
Won-Ho Choi ◽  
Jun Liu
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Research Field ◽  
Personalized Recommendation ◽  
Complex Structures ◽  
Selection Efficiency ◽  
Training Set ◽  
Deep Architecture ◽  
Recommendation Algorithms ◽  
Movie Recommendation

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.

scholarly journals A Deep Learning-Based Course Recommender System for Sustainable Development in Education

2021 ◽  
Vol 11 (19) ◽  
pp. 8993
Author(s):  
Qinglong Li ◽  
Jaekyeong Kim
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Course Content ◽  
User Behaviors ◽  
Real World Datasets ◽  
High Level

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

scholarly journals Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yu Zheng ◽  
Xiaolong Xu ◽  
Lianyong Qi
Keyword(s):  
Wireless Networks ◽  
Big Data ◽  
Deep Learning ◽  
Heterogeneous Data ◽  
Image Features ◽  
Mobile Wireless Networks ◽  
Personalized Recommendation ◽  
Mobile Users ◽  
Mobile Wireless ◽  
Deep Cnn

At present, to improve the accuracy and performance for personalized recommendation in mobile wireless networks, deep learning has been widely concerned and employed with social and mobile trajectory big data. However, it is still challenging to implement increasingly complex personalized recommendation applications over big data. In view of this challenge, a hybrid recommendation framework, i.e., deep CNN-assisted personalized recommendation, named DCAPR, is proposed for mobile users. Technically, DCAPR integrates multisource heterogeneous data through convolutional neural network, as well as inputs various features, including image features, text semantic features, and mobile social user trajectories, to construct a deep prediction model. Specifically, we acquire the location information and moving trajectory sequence in the mobile wireless network first. Then, the similarity of users is calculated according to the sequence of moving trajectories to pick the neighboring users. Furthermore, we recommend the potential visiting locations for mobile users through the deep learning CNN network with the social and mobile trajectory big data. Finally, a real-word large-scale dataset, collected from Gowalla, is leveraged to verify the accuracy and effectiveness of our proposed DCAPR model.

scholarly journals CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 265 ◽  
Author(s):  
Mingrui Sun ◽  
Tengfei Min ◽  
Tianyi Zang ◽  
Yadong Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Missing Data ◽  
Clinical Data ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Personalized Recommendation ◽  
Data Prediction ◽  
Latent Features ◽  
Missing Data Prediction

(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two different publicly available clinical datasets, which has various types of inputs and different quantity of missing. We also evaluated the performance of our algorithm, using clinical datasets that were missing at random (MAR), which were missing at various degrees; and (4) Conclusions: The experimental results demonstrate that DRBM can effectively capture the latent features of real clinical data and exhibits excellent performance for predicting missing values and result classification.

scholarly journals Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

2019 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Emelia Opoku Aboagye ◽  
Rajesh Kumar
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Tensor Factorization ◽  
Scalable Systems ◽  
Collaborative Recommendation ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Cold Start Problem

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

scholarly journals Tag-Aware Personalized Recommendation Using a Hybrid Deep Model

2017 ◽  
Author(s):  
Zhenghua Xu ◽  
Thomas Lukasiewicz ◽  
Cheng Chen ◽  
Yishu Miao ◽  
Xiangwu Meng
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Feature Space ◽  
Personalized Recommendation ◽  
Neural Network Approach ◽  
Deep Model ◽  
Deep Feature ◽  
Training Quality ◽  
Training Efficiency ◽  
Better Than

Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

scholarly journals A Light Recommendation Algorithm of We-Media Articles Based on Content

2020 ◽  
Vol 12 (4) ◽  
pp. 68-81
Author(s):  
Xin Zheng ◽  
Jun Li ◽  
Qingrong Wu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Learning Algorithm ◽  
Personalized Recommendation ◽  
Combined Method ◽  
Recommendation Algorithm ◽  
Deep Learning Algorithm

Since the explosive growth of we-medias today, personalized recommendation is playing an increasingly important role to help users to find their target articles in vast amounts of data. Deep learning, on the other hand, has shown good results in image processing, computer vision, natural language processing, and other fields. But it's a relative blank in the application of we-media articles recommendation. Combining the new features of we-media articles, this paper puts forward a recommendation algorithm of we-media articles based on topic model, Latent Dirichlet Allocation (LDA), and deep learning algorithm, Recurrent Neural Networks (RNNs). Experiments on the real datasets show that the combined method outperforms the traditional collaborative filtering recommendation and non-personalized recommendation method.

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