Users' reading habits in online news portals
Published 21 August 2014
Cagdas Esiyok, Benjamin Kille, Brijnesh-Johannes Jain, Frank Hopfgartner, Şahin Albayrak
Citations23
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category.
Abstract
The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category.
Keywords
Computer SciencePhysics and Astronomy
Machine learned job recommendation
157 Citations2011Ioannis Paparrizos, B. Barla Cambazoğlu +1 more
This work trains a machine learning model using a large number of job transitions extracted from the publicly available employee profiles in the Web to predict an employee's next job transition.
From chatter to headlines
141 Citations2012Gianmarco De Francisci Morales, Aristides Gionis +1 more
The results show that the combination of various signals from real-time Web and micro-blogging platforms can be a useful resource to understand user behavior.
Ontology-Based Personalised and Context-Aware Recommendations of News Items
101 Citations2008Iván Cantador, Alejandro Bellogín +1 more
A model that personalizes the order in which news articles are shown to the user according to his long-term interest profile is evaluated, and other model that reorders the news items lists taking into account the current semantic context of interest of the user is investigated.
A location-based news article recommendation with explicit localized semantic analysis
91 Citations2013Jeong-Woo Son, A-Yeong Kim +1 more
The Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents that stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA.
The plista dataset
87 Citations2013Benjamin Kille, Frank Hopfgartner +2 more
This work presents the plista dataset, a collection of news articles published on 13 news portals that comprises user interactions with those articles and explains the dataset's main characteristics.
Lecture notes in computer scienceAdaptive News Access
70 Citations2007Daniel Billsus, Michael J. Pazzani
This chapter describes how the adaptive web technologies discussed in this book have been applied to news access and outlines a user modeling approach specifically designed for news personalization, and presents results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective.
Comparing and evaluating information retrieval algorithms for news recommendation
64 Citations2007Toine Bogers, Antal van den Bosch
It is argued that the performance of content-based news recommender systems has been hampered by using relatively old and simple matching algorithms and that using more current probabilistic retrieval algorithms results in significant performance boosts.
Lecture notes in computer scienceNews@hand: A Semantic Web Approach to Recommending News
61 Citations2008Iván Cantador, Alejandro Bellogín +1 more
News@hand is presented, a news recommender system which applies semantic-based technologies to describe and relate news contents and user preferences in order to produce enhanced recommendations.
Lecture notes in computer scienceBenchmarking News Recommendations in a Living Lab
46 Citations2014Frank Hopfgartner, Benjamin Kille +4 more
It is argued that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems and the experimental setup of the two benchmarking events is outlined.
Machine LearningLearning to rank on graphs
35 Citations2010Shivani Agarwal
This work builds on the graph regularization ideas developed in the context of other graph learning problems, and learns a ranking function in a reproducing kernel Hilbert space (RKHS) derived from the graph that allows it to show attractive stability and generalization properties.
Semantic news recommendation using wordnet and bing similarities
33 Citations2013Michel Capelle, Frederik Hogenboom +2 more
This work extends the state-of-the-art semantic lexicon-driven Semantic Similarity (SS) recommendation method by additionally considering named entities, and shows that the recommendation method, BingSS, outperforms SS in terms of F1, precision, accuracy, and specificity.
Workshop and challenge on news recommender systems
20 Citations2013Mozhgan Tavakolifard, Jon Atle Gulla +8 more
This workshop brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems and offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.
ACM Transactions on Intelligent Systems and TechnologyA modified random walk framework for handling negative ratings and generating explanations
13 Citations2013Yu‐Chih Chen, Yu-Shi Lin +2 more
This article presents a novel and efficient RW-based model that can handle both positive and negative comments with the guarantee of convergence, and argues that a good recommendation system should provide users not only a list of recommended items but also reasonable explanations for the decisions.