research-article

Learning to model relatedness for news recommendation

Authors:

Yi ChangAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference on World wide web

Pages 57 - 66

https://doi.org/10.1145/1963405.1963417

Published: 28 March 2011 Publication History

Abstract

With the explosive growth of online news readership, recommending interesting news articles to users has become extremely important. While existing Web services such as Yahoo! and Digg attract users' initial clicks by leveraging various kinds of signals, how to engage such users algorithmically after their initial visit is largely under-explored. In this paper, we study the problem of post-click news recommendation. Given that a user has perused a current news article, our idea is to automatically identify "related" news articles which the user would like to read afterwards. Specifically, we propose to characterize relatedness between news articles across four aspects: relevance, novelty, connection clarity, and transition smoothness. Motivated by this understanding, we define a set of features to capture each of these aspects and put forward a learning approach to model relatedness. In order to quantitatively evaluate our proposed measures and learn a unified relatedness function, we construct a large test collection based on a four-month commercial news corpus with editorial judgments. The experimental results show that the proposed heuristics can indeed capture relatedness, and that the learned unified relatedness function works quite effectively.

References

[1]

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 17(6):734--749, 2005.

Digital Library

[2]

D. Agarwal, B.-C. Chen, and P. Elango. Explore/exploit schemes for web content optimization. In ICDM '09, pages 1--10, 2009.

Digital Library

[3]

J. Allan, C. Wade, and A. Bolivar. Retrieval and novelty detection at the sentence level. In SIGIR '03, pages 314--321, 2003.

Digital Library

[4]

D. Billsus and M. J. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2-3):147--180, 2000.

Digital Library

[5]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

[6]

T. Bogers and A. van den Bosch. Comparing and evaluating information retrieval algorithms for news recommendation. In RecSys '07, pages 141--144, 2007.

Digital Library

[7]

J. P. Callan. Passage-level evidence in document retrieval. In SIGIR '94, pages 302--310, Dublin, Ireland, 1994.

Digital Library

[8]

J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In SIGIR '98, pages 335--336, 1998.

Digital Library

[9]

S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Predicting query performance. In SIGIR '02, pages 299--306, 2002.

Digital Library

[10]

A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW '07, pages 271--280, 2007.

Digital Library

[11]

H. Fang, T. Tao, and C. Zhai. A formal study of information retrieval heuristics. In SIGIR '04, pages 49--56, 2004.

Digital Library

[12]

J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189--1232, 2000.

[13]

D. Harman. Overview of the third text retrieval conference (trec-3). In TREC, 1994.

[14]

T. Hofmann. Probabilistic latent semantic indexing. In SIGIR '99, pages 50--57, 1999.

Digital Library

[15]

K. S. Jones and C. J. van Rijsbergen. Report on the need for and the provision of an 'ideal' information retrieval test collection. Technical Report (British Library Research and Development Report No. 5266), Computer Laboratory, University of Cambridge, 1975.

[16]

M. Kaszkiel and J. Zobel. Effective ranking with arbitrary passages. Journal of the American Society for Information Science and Technology, 52(4):344--364, 2001.

[17]

K. Lang. Newsweeder: Learning to filter netnews. In in Proceedings of the 12th International Machine Learning Conference, 1995.

[18]

V. Lavrenko and W. B. Croft. Relevance-based language models. In SIGIR '01, pages 120--127, 2001.

Digital Library

[19]

J. Lin. Divergence measures based on the shannon entropy. IEEE Trans. Infor. Theory, 37:145--151, 1991.

Digital Library

[20]

T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009.

Digital Library

[21]

X. Liu and W. B. Croft. Passage retrieval based on language models. In CIKM '02, pages 375--382, McLean, Virginia, USA, 2002.

Digital Library

[22]

Y. Lv and C. Zhai. A comparative study of methods for estimating query language models with pseudo feedback. In Proceedings of CIKM '09, 2009.

Digital Library

[23]

C. Macdonald, I. Ounis, and I. Soboroff. Overview of trec-2009 blog track. In TREC '09, 2009.

[24]

J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In SIGIR '98, pages 275--281, 1998.

Digital Library

[25]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW '94, pages 175--186, 1994.

Digital Library

[26]

S. E. Robertson. The probability ranking principle in ir. pages 281--286, 1997.

Digital Library

[27]

S. E. Robertson and K. S. Jones. Relevance weighting of search terms. Journal of the American Society of Information Science, 27(3):129--146, 1976.

[28]

S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In TREC '94, pages 109--126, 1994.

[29]

J. J. Rocchio. Relevance feedback in information retrieval. In In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313--323. Prentice-Hall Inc., 1971.

Digital Library

[30]

G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society of Information Science, 41(4):288--297, 1990.

[31]

G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613--620, 1975.

Digital Library

[32]

D. Shahaf and C. Guestrin. Connecting the dots between news articles. In KDD '10, pages 623--632, 2010.

Digital Library

[33]

A. Singhal. Modern information retrieval: a brief overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 24:2001, 2001.

[34]

A. Singhal, C. Buckley, and M. Mitra. Pivoted document length normalization. In SIGIR '96, pages 21--29, 1996.

Digital Library

[35]

H. Toda and R. Kataoka. A clustering method for news articles retrieval system. In WWW '05, pages 988--989, 2005.

Digital Library

[36]

Y. Yang, N. Bansal, W. Dakka, P. Ipeirotis, N. Koudas, and D. Papadias. Query by document. In WSDM '09, pages 34--43, 2009.

Digital Library

[37]

C. Zhai, W. W. Cohen, and J. Lafferty. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In SIGIR '03, pages 10--17, 2003.

Digital Library

[38]

C. Zhai and J. D. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR '01, pages 334--342, 2001.

Digital Library

[39]

Y. Zhang, J. Callan, and T. Minka. Novelty and redundancy detection in adaptive filtering. In SIGIR '02, pages 81--88, 2002.

Digital Library

[40]

Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, and G. Sun. A general boosting method and its application to learning ranking functions for web search. In NIPS '07. 2007.

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
Rosnes DStarke ATrattner C(2024)Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity MetricsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659560(201-211)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659560
Lv PZhang QShi LGuan ZFan YLi JZhong KDeveci M(2024)Exploring on role of location in intelligent news recommendation from data analysis perspectiveInformation Sciences10.1016/j.ins.2024.120213662(120213)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120213
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cover image ACM Other conferences

WWW '11: Proceedings of the 20th international conference on World wide web

March 2011

840 pages

ISBN:9781450306324

DOI:10.1145/1963405

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

Copyright © 2011 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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New York, NY, United States

Publication History

Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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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
Rosnes DStarke ATrattner C(2024)Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity MetricsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659560(201-211)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659560
Lv PZhang QShi LGuan ZFan YLi JZhong KDeveci M(2024)Exploring on role of location in intelligent news recommendation from data analysis perspectiveInformation Sciences10.1016/j.ins.2024.120213662(120213)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120213
Zhao QChen XZhang HLi X(2024)Dynamic Hierarchical Attention Network for news recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124667255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124667
Robissout DBossuet LHabrard A(2024)Scoring the predictions: a way to improve profiling side-channel attacksJournal of Cryptographic Engineering10.1007/s13389-024-00346-414:3(513-535)Online publication date: 8-Apr-2024
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Starke ASolberg VØverhaug STrattner C(2024)Examining the merits of feature-specific similarity functions in the news domain using human judgmentsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09412-234:4(995-1042)Online publication date: 7-Aug-2024
https://doi.org/10.1007/s11257-024-09412-2
Giannakas TGiovanidis ASpyropoulos T(2022)MDP-based Network Friendly RecommendationsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35131316:4(1-29)Online publication date: 1-Apr-2022
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Wang SScells HMourad AZuccon G(2022)Seed-Driven Document Ranking for Systematic Reviews: A Reproducibility StudyAdvances in Information Retrieval10.1007/978-3-030-99736-6_46(686-700)Online publication date: 5-Apr-2022
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Modani NMaurya AVerma GNair IPatil VKanfade A(2022)Detecting Document Versions and Their Ordering in a CollectionWeb Information Systems Engineering – WISE 202110.1007/978-3-030-91560-5_30(405-419)Online publication date: 1-Jan-2022
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Giannakas TGiovanidis ASpyropoulos T(2021)SOBA: Session optimal MDP-based network friendly recommendationsIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488720(1-10)Online publication date: 10-May-2021
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