scholarly journals Research on News Recommendation System Based on Deep Network and Personalized Needs

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Weijia Zhang ◽  
Feng Ling
Keyword(s):  
Recommendation System ◽  
Poor Performance ◽  
Good Effect ◽  
Deep Network ◽  
Network Function ◽  
User Management ◽  
System Module ◽  
Interest Model ◽  
Function Module ◽  
News Recommendation

In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.

scholarly journals Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhengyou Xia ◽  
Shengwu Xu ◽  
Ningzhong Liu ◽  
Zhengkang Zhao
Keyword(s):  
Bayesian Model ◽  
Recommendation System ◽  
Research Work ◽  
Joint Probability ◽  
Real Data ◽  
Online News ◽  
City Government ◽  
Data Sets ◽  
News Websites ◽  
News Recommendation

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

News Recommendation for China Sina Weibo Microblog Service Based on User Social Behaviors

2014 ◽  
pp. 220-243
Author(s):  
Zuo Yuchu ◽  
You Fang ◽  
Wang Jianmin ◽  
Zhou Zhengle
Keyword(s):  
Social Network ◽  
Quantitative Analysis ◽  
Recommendation System ◽  
Heuristic Rules ◽  
Network Service ◽  
Social Network Service ◽  
Ranking List ◽  
Sina Weibo ◽  
Definition Of ◽  
News Recommendation

Sina weibo microblog is an increasingly popular social network service in China. In this work, the authors conducted a study of detecting news in Sina weibo microblog. They found the traditional definition for news can be generalized here. They first expanded the definition of news by conducting user surveys and quantitative analysis. The authors built a news recommendation system by modeling the users, classifying them into four different groups, and applying several heuristic rules, which derived from the generalized definition of news. By applying the new recommendation system, people got newsworthy information, while the funny and interesting tweets, which are popular in Sina weibo microblog, were put in the last ranking list. This study helps us achieve better understanding of heuristic rules about news. Some official organizations can also benefit from the work by supervising the most popular news around civilians.

Towards a journalist-based news recommendation system: The Wesomender approach

2013 ◽  
Vol 40 (17) ◽  
pp. 6735-6741 ◽  
Author(s):  
Alejandro Montes-García ◽  
Jose María Álvarez-Rodríguez ◽  
Jose Emilio Labra-Gayo ◽  
Marcos Martínez-Merino
Keyword(s):  
Recommendation System ◽  
News Recommendation

scholarly journals Incorporating popularity in a personalized news recommender system

2016 ◽  
Vol 2 ◽  
pp. e63 ◽  
Author(s):  
Nirmal Jonnalagedda ◽  
Susan Gauch ◽  
Kevin Labille ◽  
Sultan Alfarhood
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
User Profile ◽  
Online News ◽  
User Interests ◽  
News Sources ◽  
Enormous Amount ◽  
News Recommendation

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, “Twitter.” News articles are ranked based on the popularity of the article identified from Twitter’s public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile.

scholarly journals Research on Recommendation Systems using Deep Learning Models

2019 ◽  
Vol 8 (4) ◽  
pp. 10544-10551
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Models ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Future Preference ◽  
Movie Recommendation ◽  
News Recommendation

Recommender System is the effective tools that are accomplished of recommending the future preference of a set of products to the consumer and to predict the most likelihood items. Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for the interest of the consumer from many items. Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory. In recent days, various recommendation methods are applied to resolve the data abundance setback in numerous application areas like movie recommendation, e-commerce, news recommendation, song recommendation and social media. Even if all the available current recommender systems are successful in generating reasonable predictions, these recommendation system still facing the issues like accuracy, cold-start, sparsity and scalability problem. Deep learning, the recently developed sub domain of machine learning technique is utilized in recommendation systems to enhance the feature of predicted output. Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented. In this research, the basic terminologies, the fundamental concepts of Recommendation engine and a wide-ranging review of deep learning models utilized in Recommender Systems are presented.

scholarly journals Development of Liver Function Module to Understand the Students Concept in Biological Lessons

2020 ◽  
Vol 1 (1) ◽  
pp. 25-28
Author(s):  
Alvi Yulia Rahmi ◽  
Bhakti Karyadi ◽  
Hery Suhartoyo
Keyword(s):  
High School ◽  
High School Students ◽  
Liver Function ◽  
Research Method ◽  
Excretory System ◽  
School Students ◽  
Learning Resource ◽  
Learning Module ◽  
System Module ◽  
Function Module

The goal of this study was to develop an excretion system module in Biology subjects to stimulate the understanding of the concepts of high school students. The research method refers to the steps of research and Development. The research begins by analyzing the need of the excretory system material to be used as a learning resource for students. The trial module was limited to 20 high school students in Kepahiang District who had received excretion system material. The result showed that the ability to understand the concepts of students varied greatly, the ability to understand the concepts of students was mostly in the good category (35%), and sufficient (65%). The ability of students in aspect of understanding an idea, translating relationship that exist in a symbol, illustration, map, diagram, table, graph, has been well developed (translation). The ability to develop and obtain information that is not explicitly listed from the referenced source has been well developed (interpretation), and the ability to predict or give an idea of something based on trends that apper in the data that has not been well developed (extrapolation). In summary, the excretory system learning module is capable of stimulating the ability to understand the concept of students in terms of classical values.

Study on Network Video Monitoring System for Industrial Field

2014 ◽  
Vol 536-537 ◽  
pp. 249-252
Author(s):  
Zhu Wang ◽  
Miao Miao Liu
Keyword(s):  
Monitoring System ◽  
Full Range ◽  
Remote Access ◽  
Video Monitoring ◽  
Main Function ◽  
Functional Requirements ◽  
Module Design ◽  
Overall Design ◽  
User Management ◽  
Function Module

This paper introduces the IP network video monitoring system for monitoring and management of industrial field. All the signals in this monitoring system are transmitted through the network, without additional wiring. It not only can transmit images, alarm information, also the focus of a number of functions, such as remote monitoring, video playback, query system settings and user management. The system overall design principle, structural design, functional requirements, the main function module design are described. Through the monitoring system, managers can whenever and wherever real-time monitoring equipment working state, remote access management, provide a full range of decision support for production.

Sign in / Sign up

Export Citation Format

Share Document