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 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 Clothing Recommendation Based on User Emotional Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xueping Su ◽  
Meng Gao ◽  
Jie Ren ◽  
Yunhong Li ◽  
Matthias Rätsch
Keyword(s):  
Facial Expression ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Continuous Development ◽  
Expression Intensity ◽  
Novel Method ◽  
Multiclass Svm

With the continuous development of economy, consumers pay more attention to the demand for personalization clothing. However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. When browsing online clothing, facial expression is the salient information to understand the user’s preference. In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis. Firstly, the facial expression is classified by multiclass SVM. Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN. Finally, the multi-interest value is fused to carry out personalized recommendation. The experimental results show that the proposed method achieves a significant improvement over other algorithms.

An Improved Personalized Recommendation System Research

2013 ◽  
Vol 756-759 ◽  
pp. 1398-1402
Author(s):  
Xing Yuan Li ◽  
Qing Shui Li
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Information Services ◽  
Information Resources ◽  
Personalized Recommendation ◽  
System Research ◽  
Rating Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

scholarly journals Need of Sentiments Analysis with CF for Quality Recommendations

2021 ◽  
Vol 5 (2) ◽  
pp. 1-5
Author(s):  
Seema P. Nehete ◽  
Satish R. Devane
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Research Papers ◽  
High Recall ◽  
User Reviews ◽  
Target User ◽  
Artificial Neural Network Ann ◽  
Quality Of Products ◽  
The Right

Recommendation system (RS) help user for purchasing the right product of their interest within the affordable right price. Presently many RS make use of only filtering methods to recommend products to the user which is not taking care of the quality of products. Quality of products can be found from textual reviews available on various e-commerce websites and hence this RS performs Sentiment Analysis (SA)of extracted relevant textual reviews along with Collaborative Filtering (CF) to give accurate and good quality recommendations to the user. Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.CF performance is proved by using the standard dataset of movilense used in many research papers. Results show high recall and accuracy of CF for the recommendation of products to the target user.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

Personalized Recommendation Based on Contextual Awareness and Tensor Decomposition

2018 ◽  
Vol 16 (3) ◽  
pp. 39-51
Author(s):  
Zhenjiao Liu ◽  
Xinhua Wang ◽  
Tianlai Li ◽  
Lei Guo
Keyword(s):  
Recommendation System ◽  
Tensor Decomposition ◽  
Personalized Recommendation ◽  
Third Order ◽  
Recommendation Algorithm ◽  
Singular Value Decomposition Method ◽  
Contextual Awareness ◽  
Value Decomposition ◽  
Item Item

In order to solve users' rating sparsely problem existing in present recommender systems, this article proposes a personalized recommendation algorithm based on contextual awareness and tensor decomposition. Via this algorithm, it was first constructed two third-order tensors to represent six types of entities, including the user-user-item contexts and the item-item-user contexts. And then, this article uses a high order singular value decomposition method to mine the potential semantic association of the two third-order tensors above. Finally, the resulting tensors were combined to reach the recommendation list to respond the users' personalized query requests. Experimental results show that the proposed algorithm can effectively improve the effectiveness of the recommendation system. Especially in the case of sparse data, it can significantly improve the quality of the recommendation.

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

Study on Similar Case Determination of Personalized Recommendation System

2011 ◽  
Vol 55-57 ◽  
pp. 1494-1497
Author(s):  
Jie Li Sun ◽  
Zhi Qing Zhu ◽  
Yong Mei
Keyword(s):  
Recommendation System ◽  
Similar Case ◽  
Characteristic Vector ◽  
Personalized Recommendation ◽  
Determination Method ◽  
Case Retrieval ◽  
Case Characteristic ◽  
Case Reasoning

The quality of the recommended results will depend on the determination policy of the case similarity, case retrieval policy and personalized recommended policy based on case reasoning. The case similarity determination strategy is one of the important link to design the personalized recommendation system. This paper studies the case similarity determination method of the personalized recommendation system based-CBR . And the similar determination method based on similar case characteristic vector are discussed and the relevant algorithm is given.

Quality of life of chronically ill children from a theoretical perspective

2019 ◽  
Vol LXXX (3) ◽  
pp. 175-189
Author(s):  
Karolina Wiśniewska
Keyword(s):  
Quality Of Life ◽  
Chronic Diseases ◽  
Psychosocial Functioning ◽  
Negative Impact ◽  
Theoretical Perspective ◽  
Chronically Ill ◽  
Health Condition ◽  
Point Of View ◽  
Chronically Ill Children

In the article, I emphasize the interdisciplinary nature of the quality of life concept, which is used in many scientific disciplines. The approach to this issue depends largely on the perspective of the subject that is concerned with this problem. Quality of life changes with age, the level of self-awareness, and social roles and life tasks a person takes on. That is why I have sought to outline the determinants of quality of life and its dimensions. I stress the point that these issues are particularly important from the point of view of chronically ill children, as the development of illness may have a negative impact on their physical, mental, and social spheres. Moreover, the constantly increasing number of children with chronic diseases creates a need for a closer investigation into problems in their psychosocial functioning that are determined by their health condition. The article aims to present the issues of quality of life as well as illness and its impact on the quality of life of children with chronic diseases.

Sign in / Sign up

Export Citation Format

Share Document