scholarly journals When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 300 ◽  
Author(s):  
Cristina Gena ◽  
Pierluigi Grillo ◽  
Antonio Lieto ◽  
Claudio Mattutino ◽  
Fabiana Vernero
Keyword(s):  
Recommender Systems ◽  
Neutral Condition ◽  
Online Information ◽  
In The Wild ◽  
Ad Populum ◽  
Logical Fallacies ◽  
News Recommendation ◽  
Information Providers ◽  
Wide Access ◽  
The Way

Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing.

Online information retrieval behaviour and economics of attention

2015 ◽  
Vol 39 (6) ◽  
pp. 779-794 ◽  
Author(s):  
Mustafa Utku Özmen
Keyword(s):  
Information Retrieval ◽  
Information Provision ◽  
General Information ◽  
Survival Duration ◽  
Online Information ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Quasi Experimental ◽  
Information Providers

Purpose – The purpose of this paper is to analyse users’ attitudes towards online information retrieval and processing. The aim is to identify the characteristics of information that better capture the attention of the users and to provide evidence for the information retrieval behaviour of the users by studying online photo archives as information units. Design/methodology/approach – The paper analyses a unique quasi-experimental data of photo archive access counts collected by the author from an online newspaper. In addition to access counts of each photo in 500 randomly chosen photo galleries, characteristics of the photo galleries are also recorded. Survival (duration) analysis is used in order to analyse the factors affecting the share of the photo gallery viewed by a certain proportion of the initial number of viewers. Findings – The results of the survival analysis indicate that users are impatient in case of longer photo galleries; they lose attention faster and stop viewing earlier when gallery length is uncertain; they are attracted by keywords and initial presentation and they give more credit to specific rather than general information categories. Practical implications – Results of the study offer applicable implications for information providers, especially on the online domain. In order to attract more attention, entities can engage in targeted information provision by taking into account people’s attitude towards information retrieval and processing as presented in this paper. Originality/value – This paper uses a unique data set in a quasi-experimental setting in order to identify the characteristics of online information that users are attracted to.

A Semantic Approach for News Recommendation

2011 ◽  
pp. 102-121 ◽  
Author(s):  
Flavius Frasincar ◽  
Wouter IJntema ◽  
Frank Goossen ◽  
Frederik Hogenboom
Keyword(s):  
Recommender Systems ◽  
Similarity Measures ◽  
Extraction Methods ◽  
Decision Processes ◽  
Semantic Approach ◽  
Business Decision ◽  
Term Extraction ◽  
Cosine Similarity Measure ◽  
News Recommendation ◽  
F Measure

News items play an increasingly important role in the current business decision processes. Due to the large amount of news published every day it is difficult to find the new items of one’s interest. One solution to this problem is based on employing recommender systems. Traditionally, these recommenders use term extraction methods like TF-IDF combined with the cosine similarity measure. In this chapter, we explore semantic approaches for recommending news items by employing several semantic similarity measures. We have used existing semantic similarities as well as proposed new solutions for computing semantic similarities. Both traditional and semantic recommender approaches, some new, have been implemented in Athena, an extension of the Hermes news personalization framework. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the new semantic recommenders have a better F-measure than existing semantic recommenders.

scholarly journals Recommendation as Collaboration in Web Search

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 35-45 ◽  
Author(s):  
Barry Smyth ◽  
Jill Freyne ◽  
Maurice Coyle ◽  
Peter Briggs
Keyword(s):  
Recommender Systems ◽  
Search Engines ◽  
Web Search ◽  
Online Information ◽  
Information Discovery ◽  
Proactive Approach ◽  
Systems Research ◽  
Traditional Approaches ◽  
New Generation

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

scholarly journals The Impact of Distance Education on Libraries

2009 ◽  
Vol 2 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Kenneth A. Boyd
Keyword(s):  
Higher Education ◽  
Distance Education ◽  
Information Literacy ◽  
The Internet ◽  
Online Information ◽  
Theological Seminary ◽  
Technological Advances ◽  
The Impact ◽  
The Way

Technological advances and the Internet have radically changed the way people learn, live, and grow. In higher education, libraries have been challenged to look at how to serve people not only locally but at a distance. At Asbury Theological Seminary these changes have revolved around three issues: providing the same resources online, information literacy, and the importance of collaboration.

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 Mutual Influence of Users Credibility and News Spreading in Online Social Networks

2021 ◽  
Vol 13 (5) ◽  
pp. 107
Author(s):  
Vincenza Carchiolo ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni ◽  
Marialaura Previti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Mutual Influence ◽  
Collective Intelligence ◽  
Online Information ◽  
Fake News ◽  
Potential Growth ◽  
Multiplex Network ◽  
Time News ◽  
Information Providers

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

Computing Recommendations with Collaborative Filtering

2011 ◽  
pp. 23-41
Author(s):  
Neal Lathia
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Research ◽  
Filtering Algorithms ◽  
Future Developments ◽  
The Past ◽  
Current Trends ◽  
The Future ◽  
The Way

Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.

THE ELEMENT OF LIVING STORM: SWINBURNE AND THE BRONTËS

2013 ◽  
Vol 41 (3) ◽  
pp. 463-485
Author(s):  
Lakshmi Krishnan
Keyword(s):  
Algernon Charles Swinburne ◽  
Wuthering Heights ◽  
Southern England ◽  
The North ◽  
In The Wild ◽  
The Way

That Algernon Charles Swinburne loved the Brontës is well known, and his interest in them well documented. His admiration for Charlotte and Emily, in particular, prompted two studies, a short book and an article, which were instrumental in establishing their critical reputation as it exists today. “Those great twin sisters in genius,” as he wrote in 1877, held a powerful sway over Swinburne's imagination (A Note 188–200). He considered them his Yorkshire kinswomen, bred in the wild borderlands of the North (although Swinburne was born in London and spent most of his life in southern England, his family was based in Northumberland, and he never lost his allegiance to the county, calling himself a “Borderer” to the very end). He sensed in their work – Emily's especially – the haunting, poetic influence of the moors, a passionate, romantic spirit that saturated his own verse and prose. More, they were his novelistic predecessors, and his essays on them shed considerable light on his own fictional practice. In framing himself as the Brontës’ apologist, Swinburne was “far ahead of his time,” shaping Victorian criticism (Hyder 15–16). His praise of Wuthering Heights is considered “by some literary historians to be epochmaking” and altered the way in which novels were discussed, analysed, and ultimately evaluated (Watson 247). There are also striking features that suggest Swinburne's own novel Lesbia Brandon – in its trans-genre form and unique milieu – was conceived as an exercise in the manner of Wuthering Heights.

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