scholarly journals News Recommendation Systems in the Era of Information Overload

2021 ◽  
Author(s):  
Shuaishuai Feng ◽  
Junyan Meng ◽  
Jiaxing Zhang
Keyword(s):  
Information Overload ◽  
Recommendation Systems ◽  
Mobile Internet ◽  
Modern World ◽  
The Internet ◽  
The Public ◽  
Recommendation Algorithms ◽  
Cold Starts ◽  
News Recommendation ◽  
Source Of Information

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

scholarly journals Design of Personalized Recommendation Algorithms based on Big Data Framework

2021 ◽  
Vol 2138 (1) ◽  
pp. 012025
Author(s):  
Fang Liu
Keyword(s):  
Big Data ◽  
Information Overload ◽  
Recommendation Systems ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Processing Technologies ◽  
Data Framework ◽  
Recommendation Algorithms ◽  
Data Environment ◽  
Better Than

Abstract The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

MENGAMBIL MANFAAT MEDIA SOSIAL DALAM PENGEMBANGAN LAYANAN

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Cahyana Kumbul Widada
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Positive Influence ◽  
Online Discussions ◽  
Social Bond ◽  
Modern World ◽  
The Internet ◽  
Service Model ◽  
Source Of Information

<p class="Pa6">Information technology has brought changes in all aspects of life in today’s modern world. The Internet is becoming more popular and familiar as a source of information. Social media is a medium on the internet that allows users to represent themselves as well as interact, cooperate, share, communicate with other users and form a virtual social bond. The type of social media with all its characteristics brings a positive influence in building the service model. In research at Library of Muhammadiyah University of Surakarta (UMS) the users of social media facebook (29%), Youtube (22%), Instagram (21%), twitter (17%), Blog (7%), and wiki (4%). Facebook is the most popular of social media, allowing it to be developed as a means of meeting and online discussions that interact with each other</p>

Recommender Systems

2013 ◽  
Vol 4 (4) ◽  
pp. 32-46 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis
Keyword(s):  
Recommender Systems ◽  
Rapid Growth ◽  
Information Overload ◽  
Recommendation Systems ◽  
The Internet ◽  
Valuable Customer

Due to the rapid growth of the internet in conjunction with the information overload problem the use of recommender systems has started to become necessary for both e-businesses and customers. However there are other factors such as privacy and trust that make customers suspicious. This paper gives an overview of recommendation systems, the benefits that both the business and the customers have and an explanation of the challenges, which if faced can make the personalization process better for both parties. Moreover an outline of current studies is given along with an overview of Amazon's recommendations in order to clarify that the use of recommender systems is beneficial for an e-business in many ways and also for a valuable customer of such business.

scholarly journals Personalized Recommendation via Suppressing Excessive Diffusion

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Guilin Chen ◽  
Xuzhen Zhu ◽  
Zhao Yang ◽  
Hui Tian
Keyword(s):  
Cross Validation ◽  
Information Overload ◽  
Recommendation Systems ◽  
Modern Society ◽  
Mass Diffusion ◽  
Personalized Recommendation ◽  
Similarity Estimation ◽  
Recommendation Algorithms ◽  
Benchmark Datasets ◽  
Fold Cross Validation

Efficient recommendation algorithms are fundamental to solve the problem of information overload in modern society. In physical dynamics, mass diffusion is a powerful tool to alleviate the long-standing problems of recommendation systems. However, popularity bias and redundant similarity have not been adequately studied in the literature, which are essentially caused by excessive diffusion and will lead to similarity estimation deviation and recommendation performance degradation. In this paper, we penalize the popular objects by appropriately dividing the popularity of objects and then leverage the second-order similarity to suppress excessive diffusion. Evaluation on three real benchmark datasets (MovieLens, Amazon, and RYM) by 10-fold cross-validation demonstrates that our method outperforms the mainstream baselines in accuracy, diversity, and novelty.

scholarly journals Aplicación de Metodología SCRUM para el Desarrollo de Aplicación Móvil

2019 ◽  
pp. 31-37
Author(s):  
Alicia Cortés-García ◽  
Abril Alyse Hernández-Trejo
Keyword(s):  
Social Networks ◽  
Young People ◽  
Mobile Application ◽  
Mobile Internet ◽  
Public Institution ◽  
The Internet ◽  
The Public ◽  
Agile Methodology ◽  
Internet Users ◽  
The University

Currently, young people have a strong attraction for games and social networks developed for mobile devices, so much is the demand that in 2017 the count of mobile Internet users was made; This survey showed that there are more than 3.5 billion users spending an average of 69% of their time a day on their smartphone, that is equivalent to more than 16 hours a day on the Internet. The previous statistics guided us to develop a Mobile Application in Android, with a game-like interface, since it is sought to be the closest thing to what a user with access to a smartphone frequents in their day to day; The project aims to help all students of the public institution, generating skills among themselves on the knowledge acquired throughout their stay at the University. The development of the Mobile Application was carried out under the SCRUM Agile Methodology; It is standing out above the others thanks to its easy implementation and obtaining the expected results. This work shows the process of implementing the methodology and the favorable results that were obtained when using it.

scholarly journals Measuring the Interestingness of News Articles

2011 ◽  
pp. 1194-1199
Author(s):  
Raymond K. Pon ◽  
Alfonso F. Cardenas ◽  
David J. Buttler
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Recommendation Systems ◽  
Online News ◽  
Personalized Recommendation ◽  
Government Officials ◽  
Definition Of ◽  
News Recommendation ◽  
Keyword Searches ◽  
User Personalization

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user (Carreira et al., 2004). Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article may not be interesting for various reasons, such as the article’s age or if it discusses an event that the user has already read about in other articles. Although it has been shown that collaborative filtering can aid in personalized recommendation systems (Wang et al., 2006), a large number of users is needed. In a limited user environment, such as a small group of analysts monitoring news events, collaborative filtering would be ineffective. The definition of what makes an article interesting – or its “interestingness” – varies from user to user and is continually evolving, calling for adaptable user personalization. Furthermore, due to the nature of news, most articles are uninteresting since many are similar or report events outside the scope of an individual’s concerns. There has been much work in news recommendation systems, but none have yet addressed the question of what makes an article interesting.

PROBLEMS CAUSED BY SMALL PIG FARM ODOR AROUND THE WESTERN SONGKHLA LAKE BASIN

2017 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
Papontee Teeraphan
Keyword(s):  
Air Pollution ◽  
Lake Basin ◽  
Urgent Problem ◽  
The Public ◽  
Pig Farms ◽  
Pig Farm ◽  
The Government ◽  
The Moment ◽  
Source Of Information ◽  
Proper Solutions

Pollution is currently a significant issue arising awareness throughout the world. In Thailand, pollution can often be seen in any part of the country. Air pollution is pointed as an urgent problem. This pollution has not damaged only to human health and lives, it has destroyed environment, and possibly leading to violence. In Phattalung, air pollution is affecting to the residents’ lives. Especially, when the residents who are mostly agriculturists have not managed the waste resulted from the farm. In Phattalung, at the moment, there are many pig farms, big and small. Some of them are only for consuming for a family, some, however, are being consumed for the business which pigs will be later purchased by big business companies. Therefore, concerning pollution, the researcher and the fund giver were keen to focus on the points of the air pollution of the small pig farms. This is because it has been said that those farms have not been aware on the pollution issue caused by the farms. Farm odor is very interesting which can probably lead to following problems. The researcher also hopes that this research can be used as a source of information by the government offices in order to be made even as a policy or a proper legal measurement. As the results, the study shows that, first, more than half of the samples had smelled the farm odor located nearby their communities, though it had not caused many offenses. Second, the majority had decided not to act or response in order to solve the odor problem, but some of them had informed the officers. The proper solutions in reducing offenses caused by pig farm odor were negotiation and mediation. Last, the majority does not perceive about the process under the Public Health Act B.E. 2535.

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