Users' reading habits in online news portals

Esiyok, Cagdas; Kille, Benjamin; Jain, Brijnesh-Johannes; Hopfgartner, Frank; Albayrak, Şahin

doi:10.1145/2637002.2637038

Cited by 22 publications

(23 citation statements)

References 14 publications

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“…In order to learn more about users' reading behavior and lead the design of the news recommenders, certain works performed highscale user log analysis over time [11,24,26].…”

Section: News Reading Behavior Analysismentioning

confidence: 99%

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Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

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News organizations employ personalized recommenders to target news articles to speci c readers and thus foster engagement. Existing approaches rely on extensive user pro les. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined shortterm and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

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“…In order to learn more about users' reading behavior and lead the design of the news recommenders, certain works performed highscale user log analysis over time [11,24,26].…”

Section: News Reading Behavior Analysismentioning

confidence: 99%

“…national news are read more during elections) and, to certain extent, that the individual users' interests follow those of the general public. Esiyok et al [11] analyzed immediate transitions between categories.…”

Section: News Reading Behavior Analysismentioning

confidence: 99%

Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

Self Cite

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“…In other words, users were accessing the sites following their own personal agendas. A preliminary analysis of users' behavior is performed by Esiyok et al [13] and Said et al [28]. Concluding, we argue that the average users of this living lab are Germanspeaking Europeans who follow their own information need on a diverse set of news portals.…”

Section: -5000mentioning

confidence: 78%

Benchmarking News Recommendations in a Living Lab

Hopfgartner

Kille

Lommatzsch

et al. 2014

Lecture Notes in Computer Science

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Abstract. Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys'13 and then as campaign-style evaluation lab NEWSREEL at CLEF'14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems.

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“…For e-commerce and telecommunication related pages the peak is around 11 am, and then it decreases a bit for the rest of the day. It indicates that the audience reactions also depend on the content and characteristics of the page [5,33,10,43]. We give more detailed results on audience reaction analysis in Evaluation Section 6.…”

Section: Fig 2: Audience Reaction Behaviormentioning

confidence: 99%

Toward maximizing the visibility of content in social media brand pages: a temporal analysis

Kumar

Ande

Shirish

et al. 2018

Soc. Netw. Anal. Min.

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A large amount of content is generated everyday in social media. One of the main goals of content creators is to spread their information to a large audience. There are many factors that affect information spread, such as posting time, location, type of information, number of social connections, etc. In this paper, we look at the problem of finding the best posting time(s) to get high content visibility. The posting time is derived taking other factors into account, such as location, type of information, etc. In this paper, we do our analysis over Facebook pages. We propose six posting schedules that can be used for individual pages or group of pages with similar audience reaction profile. We perform our experiment on a Facebook pages dataset containing 0.3 million posts, 10 million audience reactions. Our best posting schedule can lead to seven times more number of audience reactions compared to the average number of audience reactions that users would get without following any optimized posting schedule. We also present some interesting audience reaction patterns that we obtained through daily, weekly and monthly audience reaction analysis.

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