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 Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yange Hao ◽  
Na Song
Keyword(s):  
Recommendation System ◽  
Performance Test ◽  
Scene Perception ◽  
Search Space ◽  
Test Method ◽  
Planning System ◽  
Personalized Recommendation ◽  
Travel Planning ◽  
Key Technologies ◽  
Online Travel

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.

Content Personalized Recommendation Engine to Support an Informal Learning Environment in the Health Context

2016 ◽  
pp. 451-482
Author(s):  
Alisson Alan Lima da Costa ◽  
Francisco Milton Mendes Neto ◽  
Enio Lopes Sombra ◽  
Jonathan Darlan Cunegundes Moreira ◽  
Rafael Castro de Souza ◽  
...  
Keyword(s):  
Informal Learning ◽  
Chronic Diseases ◽  
Recommendation System ◽  
Health Condition ◽  
Personalized Recommendation ◽  
Web 2.0 Tools ◽  
Construction Of Knowledge ◽  
The Right ◽  
Informal Learning Environment

People with chronic diseases suffer with limitations imposed by their health condition and learn more about the disease helps in improving the quality of life. This is possible because the use in mass of mobile devices and the advent of Web 2.0 tools, which gave rise to the Health 2.0 concept. This search for the construction of knowledge by stimulating citizens to be active and responsible for their health. However, provide contextualized knowledge at the right time, it is not a trivial task due to the diversity of content and user's profiles. The solution to this is to provide informal learning through personalized recommendation of content by providing relevant content to users related to their health. This chapter proposes a personalized recommendation system of content, which includes the union of different recommendation techniques and genetic algorithm, seeking efficacy on the recommendation of the contents to people with chronic diseases aiming informal learning in health.

scholarly journals Performance Evaluation of DLSARS Framework in Intelligent Product Recommendation Systems

2020 ◽  
pp. 165-175
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Recommendation System ◽  
The Other ◽  
Program Model ◽  
Learning Models ◽  
Technical Approach ◽  
Huge Number ◽  
Intelligent Product ◽  
The Right

The recommendation framework is vital tool for efficient E-commerce contacts between customers and retailers. Efficient and friendly contacts to find the right product have a huge effect on the sales results. In the basis of a technical approach, four of the program model guidelines are: collective filtering, content-based and demographic filtering. Collaborative filtering is considered superior to other methods in the list. Of necessity, in terms of fortuity, novelty and precision, it provides advantages. The DLSARS Framework is a deep learning-based sentiment analysis for the DLSARS recommendation system that uses deep learning models for a proposed system. The dataset selected for this research is synthetic dataset which consists of huge number of reviews for every product. The proposed models display superiorities and compare the findings with other existing models. The proposed DLSARS frame with bigram approach is superior to the other domain on the E-commerce domain.

scholarly journals Implementasi Metode Euclidean Distance untuk Rekomendasi Ukuran Pakaian pada Aplikasi Ruang Ganti Virtual

2018 ◽  
Vol 5 (2) ◽  
pp. 129
Author(s):  
Rezky Rizaldi ◽  
Arik Kurniawati ◽  
Cucun Very Angkoso
Keyword(s):  
Augmented Reality ◽  
Euclidean Distance ◽  
Recommendation System ◽  
Three Dimensional ◽  
Positive Influence ◽  
The Body ◽  
Online Store ◽  
3 Dimensional ◽  
Potential Buyer ◽  
The Right

<p class="Abstrak">Perkembangan jual beli garmen secara <em>online</em>, dihadapkan pada kenyataan adanya 70% pengembalian produk oleh pembeli, akibat ketidaksesuaian antara harapan dan kenyataan model serta ukuran garmen. Kehadiran <em>virtual fitting room</em> secara <em>online</em>, diharapkan mampu mengurangi adanya pengembalian produk, memberikan pengaruh positif terhadap keistimewaan suatu produk, keinginan untuk membeli dan kepastian membeli secara <em>online</em>. <em>Virtual Fitting Room</em> ini bisa diimplementasikan pada toko <em>online</em> ataupun toko baju seperti biasa. Tahapan penelitian meliputi : penerapan teknologi <em>kinect</em> untuk mendapatkan data <em>skeleton</em> dari calon pembeli yang digunakan sebagai dasar untuk memberikan rekomendasi ukuran pakaian, selanjutnya perhitungan <em>euclidean distance</em> digunakan untuk menghitung ukuran punggung calon pembeli dan terakhir penerapan teknologi <em>augmented reality</em> untuk menampilkan pakaian <em>virtual</em> 3 dimensi yang melekat tepat di badan calon pembeli. Sistem rekomendasi ini mampu menampilkan calon pembeli dengan menggunakan baju virtual 3 dimensi yang sesuai dengan ukuran rekomendasi dari sistem (S,M,L, atau XL). Sistem ini juga memberikan fitur bagi calon pembeli untuk mencoba model pakaian lainnya. Sistem dapat memperlihatkan baju virtual 3 dimensi yang tetap melekat pada badan calon pembeli, ketika melakukan rotasi ke kanan 90<sup>0</sup>, ke kiri 90<sup>0</sup>, balik kanan 180<sup>0</sup> dan balik kiri 180<sup>0</sup>. Hasil uji coba sistem rekomendasi ukuran pakaian ini akan berjalan secara optimal jika pengaturan ketinggian <em>kinect</em> sebesar 55 cm dari tanah. Untuk ketinggian <em>kinect</em> 55cm, 65cm dan 75 cm dari tanah, sistem ini mampu menyajikan kesesuaian rekomendasi ukuran dibandingkan dengan ukuran asli dari calon pembeli sebesar 70%.</p><p class="Abstrak"> </p><p><strong>Kata kunci</strong>: <em>k</em><em>inect, augmented reality, euclidean distance</em><em>, virtual fitting room</em><strong></strong></p><p class="Judul2"> </p><p class="Judul2"><em>Abstract</em></p><p class="Judul2"><em>The development of online garment sale, faced with the fact that there is 70% return of product by the buyer, due to a mismatch between expectation and reality of model and garment size. The presence of virtual fitting room in the online store is expected to reduce the return of products, give a positive influence on the privilege of a product, the desire to buy and certainty to buy online. Virtual Fitting Room can be implemented in the online store or clothing store as usual. The research stages include the application of Kinect technology to obtain skeleton data from prospective buyers used as a basis for providing system recommendations, then euclidean distance calculation is used to calculate the size back potential buyers, and lastly application of augmented reality technology to display the right three-dimensional virtual clothing in potential buyer body. This recommendation system can present potential buyers by using 3-dimensional virtual shirts attached to their bodies by the recommended size of the system (S, M, L, or XL). This system also provides features for potential buyers to try other clothing models. The system can show a 3-dimensional virtual shirt that remains attached to the body of potential buyers, while rotating right 90<sup>0</sup>, left 90<sup>0</sup>, right turn 180<sup>0</sup> and left turn 180<sup>0</sup>. The test results of this clothing size recommendation system will run optimally if the Kinect height setting of 55 cm from the ground. For the Kinect height of 55cm, 65cm and 75cm from the ground, the system can present the recommended size with the original size of the potential buyer of 70%.</em></p><p class="Judul2"> </p><p><strong>Keywords</strong>: <em>kinect, augmented reality, euclidean distance, virtual fitting room</em></p>

scholarly journals Apparel-based deep learning system design for apparel style recommendation

2019 ◽  
Vol 31 (3) ◽  
pp. 376-389 ◽  
Author(s):  
Congying Guan ◽  
Shengfeng Qin ◽  
Yang Long
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Learning Technologies ◽  
The Body ◽  
Learning System ◽  
The Other ◽  
Training Data ◽  
Support Vector ◽  
Content Type

Purpose The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion. Findings The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

scholarly journals Applying Internet information technology combined with deep learning to tourism collaborative recommendation system

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240656
Author(s):  
Meng Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommendation System ◽  
Product Information ◽  
Tourism Industry ◽  
Word Embedding ◽  
Personalized Recommendation ◽  
Collaborative Recommendation ◽  
Factorization Machine ◽  
Tourism Service

Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system.

scholarly journals Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

2022 ◽  
Vol 2146 (1) ◽  
pp. 012007
Author(s):  
Yu’e Liu
Keyword(s):  
Deep Learning ◽  
Resource Management ◽  
Human Resource ◽  
Management System ◽  
Recommendation System ◽  
Pivotal Role ◽  
Personalized Recommendation ◽  
High Quality ◽  
Product Recommendation ◽  
New Type

Abstract Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Sign in / Sign up

Export Citation Format

Share Document