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.

Hot News Recommendation System across Heterogonous Websites Using Hadoop

2014 ◽  
Vol 989-994 ◽  
pp. 4704-4707
Author(s):  
Sheng Wu Xu ◽  
Zheng You Xia
Keyword(s):  
Recommendation System ◽  
Research Work ◽  
Big Data Analytics ◽  
Data Set ◽  
Set Size ◽  
News Websites ◽  
News Website ◽  
News Recommendation ◽  
News Services ◽  
Excellent Tool

The current most news recommendations are suitable for news which comes from a single news website, not for news from different news websites. Little research work has been reported on utilizing hundreds of news websites to provide top hot news services for group customers (e.g. Government staffs). In this paper, we present hot news recommendation system based on Hadoop, which is from hundreds of different news websites. We discuss our news recommendation system architecture based on Hadoop.We conclude that Hadoop is an excellent tool for web big data analytics and scales well with increasing data set size and the number of nodes in the cluster. Experimental results demonstrate the reliability and effectiveness of our method.

scholarly journals E-Learning Recommendation System for Big Data Based on Cloud Computing

2021 ◽  
Vol 16 (21) ◽  
pp. 177
Author(s):  
Mounia Rahhali ◽  
Lahcen Oughdir ◽  
Youssef Jedidi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Academic Success ◽  
Recommendation System ◽  
Learning Experience ◽  
Research Work ◽  
Cloud Services ◽  
Data Sets ◽  
E Learning ◽  
Processing Optimization

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.

scholarly journals JMASM 51: Bayesian Reliability Analysis of Binomial Model – Application to Success/Failure Data

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
M. Tanwir Akhtar ◽  
Athar Ali Khan
Keyword(s):  
Bayesian Model ◽  
Real Data ◽  
Model Parameters ◽  
Data Sets ◽  
Binomial Model ◽  
Model Application ◽  
Simulation Tools ◽  
Failure Data ◽  
Simulation Techniques ◽  
Bayesian Paradigm

Reliability data are generated in the form of success/failure. An attempt was made to model such type of data using binomial distribution in the Bayesian paradigm. For fitting the Bayesian model both analytic and simulation techniques are used. Laplace approximation was implemented for approximating posterior densities of the model parameters. Parallel simulation tools were implemented with an extensive use of R and JAGS. R and JAGS code are developed and provided. Real data sets are used for the purpose of illustration.

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 A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

2016 ◽  
Vol 16 (6) ◽  
pp. 146-159 ◽  
Author(s):  
Zhijun Zhang ◽  
Huali Pan ◽  
Gongwen Xu ◽  
Yongkang Wang ◽  
Pengfei Zhang
Keyword(s):  
Social Relations ◽  
Recommendation System ◽  
Rapid Development ◽  
Geographic Location ◽  
Real Data ◽  
Personalized Recommendation ◽  
Data Sets ◽  
Tourist Attraction ◽  
Recommendation Algorithm ◽  
Tourism Information

Abstract With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

scholarly journals Big Data-aware News Recommendation System According to Regional Twitter Users’ Interests

2021 ◽  
Author(s):  
Maryam Bagheri ◽  
Shahram Jamali ◽  
Reza Fotohi
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Research Area ◽  
Small Data ◽  
City Region ◽  
Home Pages ◽  
News Websites ◽  
Twitter Users ◽  
News Recommendation ◽  
Spark Framework

Abstract Nowadays with the development of technology and access to the Internet everywhere for everyone, the interest to get the news from newspapers and other traditional media is decreasing. Therefore, the popularity of news websites is ascending as the newspapers are changing into electronic versions. News websites can be accessed from anywhere, i.e., any country, city, region, etc. So, the need to present the news depends on where the reader is from can be a research area, as with facing with variety of news topics on websites readers prefer to choose those which more often show the news, they are interested in on their home pages. Based on this idea we represent the technique to find favorite topics of Twitter users of certain geographical districts to provide news websites a way of increasing popularity. In this work we processed tweets. It seems that tweets are some small data, but we found out that processing this small data needs a lot of time, due to the repetition of the algorithm a lot and many searches to be done. Therefore, we categorized our work as big data. To help this problem we developed our work in the Spark framework. Our technique includes 2 phases; Feature Extraction Phase and Topic Discovery Phase. Our analysis shows that with this technique we can get the accuracy between 68% and 76%, in 3 developments 3-fold, 5-fold, and 10-fold.

Reliability estimation in multicomponent stress-strength based on Erlang-truncated exponential distribution

2017 ◽  
Vol 34 (3) ◽  
pp. 438-445 ◽  
Author(s):  
Srinivasa Rao Gadde
Keyword(s):  
Sample Size ◽  
Research Work ◽  
Real Data ◽  
Small Sample ◽  
Reliability Estimation ◽  
Data Sets ◽  
Content Type ◽  
Reliability Estimates ◽  
Average Mean Square Error ◽  
Estimate Reliability

Purpose The purpose of this paper is to consider the estimation of multicomponent stress-strength reliability. The system is regarded as alive only if at least s out of k (s<k) strengths exceed the stress. The reliability of such a system is obtained when strength, stress variates are from Erlang-truncated exponential (ETE) distribution with different shape parameters. The reliability is estimated using the maximum likelihood (ML) method of estimation when samples are drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets the authors illustrate the procedure. Design/methodology/approach The authors have developed multicomponent stress-strength reliability based on ETE distribution. To estimate reliability, the parameters are estimated by using ML method. Findings The simulation results indicate that the average bias and average mean square error decreases as sample size increases for both methods of estimation in reliability. The length of the confidence interval also decreases as the sample size increases and simulated actual coverage probability is close to the nominal value in all sets of parameters considered here. Using real data, the authors illustrate the estimation process. Originality/value This research work has conducted independently and the results of the author’s research work are very useful for fresh researchers.

scholarly journals Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 992 ◽  
Author(s):  
Xu Yang ◽  
Billy Zimba ◽  
Tingting Qiao ◽  
Keyan Gao ◽  
Xiaoya Chen
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Real Data ◽  
User Preferences ◽  
Data Sets ◽  
Wireless Internet ◽  
User Reviews ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Regions

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.

Online news recommendations based on topic modeling and online interest adjustment

2019 ◽  
Vol 119 (8) ◽  
pp. 1802-1818
Author(s):  
Duen-Ren Liu ◽  
Yu-Shan Liao ◽  
Jun-Yi Lu
Keyword(s):  
Topic Modeling ◽  
Recommendation System ◽  
Online News ◽  
User Preferences ◽  
Online Media ◽  
Content Type ◽  
Online Evaluation ◽  
Important Trend ◽  
News Recommendation ◽  
Click Through Rate

Purpose Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms. Design/methodology/approach A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing. Findings This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation. Originality/value The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

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