Article

Newsjunkie: providing personalized newsfeeds via analysis of information novelty

Authors:

Evgeniy Gabrilovich,

Eric HorvitzAuthors Info & Claims

WWW '04: Proceedings of the 13th international conference on World Wide Web

Pages 482 - 490

https://doi.org/10.1145/988672.988738

Published: 17 May 2004 Publication History

Abstract

We present a principled methodology for filtering news stories by formal measures of information novelty, and show how the techniques can be usedto custom-tailor news feeds based on information that a user has already reviewed. We review methods for analyzing novelty and then describe Newsjunkie, a system that personalizes news for users by identifying the novelty of stories in the context of stories they have already reviewed. Newsjunkie employs novelty-analysis algorithms that represent articles as words and named entities. The algorithms analyze inter-andintra-document dynamics by considering how information evolves over timefrom article to article, as well as within individual articles. We review the results of a user study undertaken to gauge the value of the approachover legacy time-based review of newsfeeds, and also to compare the performance of alternate distance metrics that are used to estimate the dissimilarity between candidate new articles and sets of previously reviewed articles.

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Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Kim THan HAdar EKay MChung J(2024)Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language ArtsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642529(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642529
Chen BXu YZhen JHe XFang QCao J(2024)NRMG: News Recommendation With Multiview Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326652011:2(2245-2255)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266520
Show More Cited By

Index Terms

Newsjunkie: providing personalized newsfeeds via analysis of information novelty
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '04: Proceedings of the 13th international conference on World Wide Web

May 2004

754 pages

ISBN:158113844X

DOI:10.1145/988672

Conference Chairs:
Stuart Feldman
IBM Research
,
Mike Uretsky
New York University
,
Program Chairs:
Marc Najork
Microsoft Research
,
Craig Wills
Worcester Polytechnic Institute

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 17 May 2004

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WWW04: The 2004 World Wide Web Conference ( in conjunction with ACM Conference on Electronic Commerce [EC'04])

May 17 - 20, 2004

NY, New York, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Kim THan HAdar EKay MChung J(2024)Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language ArtsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642529(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642529
Chen BXu YZhen JHe XFang QCao J(2024)NRMG: News Recommendation With Multiview Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326652011:2(2245-2255)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3266520
Gupta KAhmad AGhosal TEkbal A(2024)SciND: a new triplet-based dataset for scientific novelty detection via knowledge graphsInternational Journal on Digital Libraries10.1007/s00799-023-00386-xOnline publication date: 8-Jan-2024
https://doi.org/10.1007/s00799-023-00386-x
Laban PWu CMurakhovs'Ka LChen XXiong C(2023)Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord QuestionsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581569(1-21)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581569
Wu CWu FHuang YXie X(2023)Personalized News Recommendation: Methods and ChallengesACM Transactions on Information Systems10.1145/353025741:1(1-50)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3530257
Meng XHuo HZhang XWang WZhu J(2023)A Survey of Personalized News RecommendationData Science and Engineering10.1007/s41019-023-00228-58:4(396-416)Online publication date: 2-Sep-2023
https://doi.org/10.1007/s41019-023-00228-5
Nair B(2023)Predicting document novelty: an unsupervised learning approachKnowledge and Information Systems10.1007/s10115-023-01989-166:3(1709-1728)Online publication date: 12-Oct-2023
https://doi.org/10.1007/s10115-023-01989-1
Ghosal TSaikh TBiswas TEkbal ABhattacharyya P(2022)Novelty Detection: A Perspective from Natural Language ProcessingComputational Linguistics10.1162/coli_a_0042948:1(77-117)Online publication date: 4-Apr-2022
https://doi.org/10.1162/coli_a_00429
Mohseni MMaher M(2022)A Framework for Exploring Computational Models of Novelty in Unstructured TextProceedings of the 6th International Conference on Information System and Data Mining10.1145/3546157.3546164(36-45)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3546157.3546164
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