poster

Do recommendations matter?: news recommendation in real life

Authors:

Alan Said,

Alejandro Bellogín,

Jimmy Lin,

Arjen de VriesAuthors Info & Claims

CSCW Companion '14: Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing

Pages 237 - 240

https://doi.org/10.1145/2556420.2556510

Published: 15 February 2014 Publication History

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Abstract

We present a study of how recommendations are received in real life by users across different news domains (traditional online newspapers, hobbyist websites, forums, etc.). Our analysis shows that readers of websites centered around specific topics are generally less likely to interact with recommendations than readers of traditional news websites.

References

[1]

Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1 (Jan. 2004), 5--53.

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[2]

Said, A., Lin, J., Bellogín, A., and de Vries, A. A month in the life of a production news recommender system. In Proceedings of the CIKM Workshop on Living Labs for Information Retrieval Evaluation, LivingLab, ACM (2013).

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[3]

Tavakolifard, M., Gulla, J. A., Almeroth, K. C., Hopfgartner, F., Kille, B., Plumbaum, T., Brodt, T., and Heintz, T. Workshop and challenge on news recommender systems. In Proceedings of the seventh ACM conference on Recommender systems, RecSys '13, ACM (New York, NY, USA, 2013).

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Cited By

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Khan MUllah KAlharbi YAlferaidi AAlharbi TYadav KAlsharabi NAhmad A(2023)Understanding the Research Challenges in Low-Resource Language and Linking Bilingual News Articles in Multilingual News ArchiveApplied Sciences10.3390/app1315856613:15(8566)Online publication date: 25-Jul-2023
https://doi.org/10.3390/app13158566
Sin JChen DThreatt JGorham AMunteanu C(2022)Does Alexa Live Up to the Hype? Contrasting Expectations from Mass Media Narratives and Older Adults' Hands-on Experiences of Voice InterfacesProceedings of the 4th Conference on Conversational User Interfaces10.1145/3543829.3543841(1-9)Online publication date: 26-Jul-2022
https://dl.acm.org/doi/10.1145/3543829.3543841
Feng CKhan MRahman AAhmad A(2020)News Recommendation Systems - Accomplishments, Challenges & Future DirectionsIEEE Access10.1109/ACCESS.2020.29677928(16702-16725)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2967792
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Index Terms

Do recommendations matter?: news recommendation in real life
1. Information systems
  1. Information retrieval

Recommendations

News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations
UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

When evaluating algorithms that recommend a list of relevant items to a user, it is common to use metrics such as precision to measure the system accuracy. When computing precision, one computes the number of items that were selected by the user among ...
A new approach to evaluating novel recommendations
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

This paper presents two methods, named Item- and User-centric, to evaluate the quality of novel recommendations. The former method focuses on analyzing the item-based recommendation network. The aim is to detect whether the network topology has any ...

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Published In

CSCW Companion '14: Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing

February 2014

372 pages

ISBN:9781450325417

DOI:10.1145/2556420

General Chairs:
Susan Fussell
Cornell University
,
Wayne Lutters
University of Maryland, Baltimore County
,
Program Chairs:
Meredith Ringel Morris
Microsoft Research
,
Madhu Reddy
Penn State University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 15 February 2014

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CSCW'14

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SIGCHI

CSCW'14: Computer Supported Cooperative Work

February 15 - 19, 2014

Maryland, Baltimore, USA

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CSCW Companion '14 Paper Acceptance Rate 88 of 497 submissions, 18%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

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October 18 - 22, 2025

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Khan MUllah KAlharbi YAlferaidi AAlharbi TYadav KAlsharabi NAhmad A(2023)Understanding the Research Challenges in Low-Resource Language and Linking Bilingual News Articles in Multilingual News ArchiveApplied Sciences10.3390/app1315856613:15(8566)Online publication date: 25-Jul-2023
https://doi.org/10.3390/app13158566
Sin JChen DThreatt JGorham AMunteanu C(2022)Does Alexa Live Up to the Hype? Contrasting Expectations from Mass Media Narratives and Older Adults' Hands-on Experiences of Voice InterfacesProceedings of the 4th Conference on Conversational User Interfaces10.1145/3543829.3543841(1-9)Online publication date: 26-Jul-2022
https://dl.acm.org/doi/10.1145/3543829.3543841
Feng CKhan MRahman AAhmad A(2020)News Recommendation Systems - Accomplishments, Challenges & Future DirectionsIEEE Access10.1109/ACCESS.2020.29677928(16702-16725)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2967792
Liao HZhang XLong ZVidmer ALiu MZhou M(2020)Diffusion-based location-aware recommender systemsJournal of Statistical Mechanics: Theory and Experiment10.1088/1742-5468/ab74c52020:4(043401)Online publication date: 8-Apr-2020
https://doi.org/10.1088/1742-5468/ab74c5
Papakonstantinou MDelis A(2019)PolyRecs: Improving Page–View Rates Using Real-Time Data AnalysisReal-Time Business Intelligence and Analytics10.1007/978-3-030-24124-7_7(95-112)Online publication date: 11-Oct-2019
https://doi.org/10.1007/978-3-030-24124-7_7
Bellogín ASaid A(2018)Recommender Systems EvaluationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110162(2095-2112)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110162
Khan MUr Rahman ADaud Awan M(2017)Term-Based Approach for Linking Digital News StoriesDigital Libraries and Multimedia Archives10.1007/978-3-319-73165-0_13(127-138)Online publication date: 21-Dec-2017
https://doi.org/10.1007/978-3-319-73165-0_13
Bellogín ASaid A(2017)Recommender Systems EvaluationEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110162-1(1-18)Online publication date: 6-Jul-2017
https://doi.org/10.1007/978-1-4614-7163-9_110162-1
Maksai AGarcin FFaltings BWerthner HZanker MGolbeck JSemeraro G(2015)Predicting Online Performance of News Recommender Systems Through Richer Evaluation MetricsProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800184(179-186)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1145/2792838.2800184
Doychev DRafter RLawlor ASmyth B(2015)News Recommenders: Real-Time, Real-Life ExperiencesUser Modeling, Adaptation and Personalization10.1007/978-3-319-20267-9_28(337-342)Online publication date: 11-Jun-2015
https://doi.org/10.1007/978-3-319-20267-9_28
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Recommendations

News Session-Based Recommendations using Deep Neural Networks

Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations

A new approach to evaluating novel recommendations

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