scholarly journals An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Saravanapriya Manoharan ◽  
Radha Senthilkumar
Keyword(s):  
Social Media ◽  
Fuzzy Rule ◽  
User Profile ◽  
Internet Technology ◽  
News Article ◽  
User Interest ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Recommendation Accuracy ◽  
News Recommendation

Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories.

scholarly journals Optimized MLCNN for Personalized News Recommendation Based on Social Media Harnessing Using Modified Genetic Algorithm

2021 ◽  
Author(s):  
Saravanapriya Manoharan ◽  
radha senthilkumar ◽  
Saktheewaran J ◽  
Kannan A
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
User Profile ◽  
Experimental Result ◽  
Breaking News ◽  
Modified Genetic Algorithm ◽  
Novel Approach ◽  
News Recommendation ◽  
Computational Resources

Abstract Classification of label-specific users’ diversified interests is the most formidable task in personalized news recommendation systems (PNRS). To bring personalization to PNRS, many remarkable features have to be considered from their user profile to classify their interest. In this paper, 13, 346 features are considered per user to classify their interest for 15 labels using Multi-label Convolution Neural Network (MLCNN). The efficiency of MLCNN highly depends on its architecture through the tuning of its hyper parameters. Generally, researchers have manually designed a constant CNN architecture for each label and every label and verified the effectiveness, but this leads to additional complexity as well as large computational resources were consumed. Moreover, Designing the structure for all 15 labels leads to an increase in network structure exponentially with an increase in labels. Hence, in this paper, MLCNN architectures are optimized by implementing a novel approach Modified Genetic Algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label-specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles were determined along with label-specific breaking news articles rendered from news feeds concerning users’ interest. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label-wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively.

VideoTopic

2014 ◽  
Vol 5 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Qiusha Zhu ◽  
Mei-Ling Shyu ◽  
Haohong Wang
Keyword(s):  
Topic Model ◽  
Optimal Solution ◽  
User Profile ◽  
Video Data ◽  
User Interest ◽  
Data Set ◽  
User Interests ◽  
Interest Representation ◽  
Benchmark Data ◽  
Recommendation Accuracy

With the vast amount of video data uploaded to the Internet every day, how to analyze user interests and recommend videos that they are potentially interested in is a big challenge. Most video recommender systems limit the content to metadata associated with videos, which could lead to poor recommendation results since the metadata is not always available or correct. On the other side, visual content of videos contain information of different granularities, from a whole video, to portions of a video, and to an object in a video, which are not fully explored. This extra information is especially important for recommending new items when no user profile is available. In this paper, a novel recommendation framework, called VideoTopic, that targets at cold-start items is proposed. VideoTopic focuses on user interest modeling and decomposes the recommendation process into interest representation, interest discovery, and recommendation generation. It aims to model user interests by using a topic model to represent the interests in the videos and then discover user interests from user watch histories. A personalized list is generated to maximize the recommendation accuracy by finding the videos that most fit the user's interests under the constraints of some criteria. The optimal solution and a practical system of VideoTopic are presented. Experiments on a public benchmark data set demonstrate the promising results of VideoTopic.

scholarly journals Social Media Usage in Recruitment Practices in Egypt

2018 ◽  
Vol 4 (3) ◽  
pp. 1
Author(s):  
Mohamed Wahba ◽  
Dalia Elmanadily
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Selection Process ◽  
User Profile ◽  
Internet Technology ◽  
Third Party ◽  
Social Media Networks ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Recruitment And Selection

Nowadays, networking within online social media platforms isn't just about swapping pictures and music, or discussing the trivial details of a night out, a TV show or a sporting event. Social media is increasingly becoming the space where professional life happens. The recent option by Face book to update user profile pages to offer a 'LinkedIn style' professional view, suggests that social media, on the whole, is becoming a medium for work as well as play.( Sophia,2009) Recruitment is a process of finding and attracting capable applicants for employment. E-recruiting is the use of internet technology to attract candidates and aid the recruitment process. This usually means using one's own company website, a third-party job site or job board, a CV database, social media or search engine marketing. Social Media recruiting (social recruiting) is the part of e-recruiting.( Palonka et al,2013) Social media in recruitment and selection occurs when recruitment representative view social networks platforms such as: LinkedIn, Facebook in the employment selection process leading to the acceptance or rejection of job applicants. The goal of this research is to assessing the recent status of the usage of social media networks in recruitment and selection process in Egyptian organizations as today social media networks and platforms provide great opportunities for business and job seekers to a certain extent. By applying an exploratory study on a random sampling procedure was used to select 200 firms from different types of sectors. The respondents of this study incorporated 130 business owners and human resources managers in Egypt through online survey. The results revealed that the 54 percentage of the respondents use social media to support their recruitment effort human while 31percent don't know and plan to use it .the results revealed that LinkedIn and FB are the most social media platforms used in recruitment , also the paper surveyed some obstacles and advantages for social media recruitment.

scholarly journals Streamlined News Recommendation System Using a Variable Markov Model

2021 ◽  
Author(s):  
Dejan Spanovic
Keyword(s):  
Markov Model ◽  
Recommendation System ◽  
News Article ◽  
User Interest ◽  
Single Model ◽  
User Interests ◽  
Single Solution ◽  
News Recommendation ◽  
And Control ◽  
To Receive

Providing news to users in a news article recommendation system is a balancing act between delivering news that is recent and news that is relevant to their interests. Users should be able to receive a stream of similar articles that interest them and control their traversal through the topics of news articles in a stream-wise fashion as well. A Variable Markov Model (VMM), built on trends in recently published news articles, is proposed as a single solution to categorically cater news to all users with minimal overhead and maintenance. This single model provided to all users throughout experimentation has shown that, though it is not built based on user interests, it is applicable as a basis for applying user interest and trend factors upon to achieve catered and novel news recommendation experiences.

scholarly journals User Modeling with Click Preference and Reading Satisfaction for News Recommendation

2020 ◽  
Author(s):  
Chuhan Wu ◽  
Fangzhao Wu ◽  
Tao Qi ◽  
Yongfeng Huang
Keyword(s):  
User Modeling ◽  
Reading Speed ◽  
News Article ◽  
User Interest ◽  
Reading Behaviors ◽  
News Content ◽  
User Behaviors ◽  
Learning Tasks ◽  
News Website ◽  
News Recommendation

Modeling user interest is critical for accurate news recommendation. Existing news recommendation methods usually infer user interest from click behaviors on news. However, users may click a news article because attracted by its title shown on the news website homepage, but may not be satisfied with its content after reading. In many cases users close the news page quickly after click. In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation. More specifically, we propose a personalized reading speed metric to measure users’ satisfaction with news content. We learn embeddings of users from the news content they have read and their satisfaction with these news to model their interest in news content. In addition, we also learn another user embedding from the news titles they have clicked to model their preference in news titles. We combine both kinds of user embeddings into a unified user representation for news recommendation. We train the user representation model using two supervised learning tasks built from user behaviors, i.e., news title based click prediction and news content based satisfaction prediction, to encourage our model to recommend the news articles which not only are likely to be clicked but also have the content satisfied by the user. Experiments on real-world dataset show our method can effectively boost the performance of user modeling for news recommendation.

scholarly journals Streamlined News Recommendation System Using a Variable Markov Model

2021 ◽  
Author(s):  
Dejan Spanovic
Keyword(s):  
Markov Model ◽  
Recommendation System ◽  
News Article ◽  
User Interest ◽  
Single Model ◽  
User Interests ◽  
Single Solution ◽  
News Recommendation ◽  
And Control ◽  
To Receive

Providing news to users in a news article recommendation system is a balancing act between delivering news that is recent and news that is relevant to their interests. Users should be able to receive a stream of similar articles that interest them and control their traversal through the topics of news articles in a stream-wise fashion as well. A Variable Markov Model (VMM), built on trends in recently published news articles, is proposed as a single solution to categorically cater news to all users with minimal overhead and maintenance. This single model provided to all users throughout experimentation has shown that, though it is not built based on user interests, it is applicable as a basis for applying user interest and trend factors upon to achieve catered and novel news recommendation experiences.

FACEBOOK SEBAGAI MEDIA BARU TAFSIR AL-QUR’AN DI INDONESIA

2018 ◽  
Vol 2 (2) ◽  
pp. 69-80
Author(s):  
Wildan Imaduddin Muhammad
Keyword(s):  
Social Media ◽  
Methodological Problem ◽  
The Self ◽  
Internet Technology ◽  
Basic Character ◽  
Hermeneutic Approach ◽  
The Times ◽  
Qur'anic Interpretation

This article analyzes the product of Salman Harun's Qur'anic  interpretation with  Facebook  as the medium. As one of the senior professors who pursue the field of interpretation, he has managed to follow the times by utilizing internet technology. There are two focus areas in the study; the first aspect of the sense of Indonesian tafsir attached to the self of Salman Harun, the two aspects of the novelty of discourse that became the basic character of social media. Both aspects are interesting to be studied with a hermeneutic approach. Given that  the  methodological problem that often arises from the hermeneutic approach is the context of the interpreter that is difficult to trace accurately, then this article finds its relevance to the case of Salman Harun's interpretation which uses the facebook media as the actualization of its interpretation product.

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