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Care to comment?: recommendations for commenting on news stories

Authors:

Ronny LempelAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 429 - 438

https://doi.org/10.1145/2187836.2187895

Published: 16 April 2012 Publication History

Abstract

Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight on Web documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users' co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the model's loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.

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. IEEE Trans. Knowl. Data Eng., 17(6):734--749, 2005.

Digital Library

[2]

D. Agarwal, B.-C. Chen, and B. Pang. Personalized recommendation of user comments via factor models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 571--582, July 2011.

Digital Library

[3]

B.-C. Chen, J. Guo, B. Tseng, and J. Yang. User reputation in a comment rating environment. In Proceedings of the 17th international conference on Knowledge discovery and data mining (KDD 2011), pages 159--167, August 2011.

Digital Library

[4]

A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 271--280, New York, NY, USA, 2007. ACM.

Digital Library

[5]

G. Dror, Y. Koren, Y. Maarek, and I. Szpektor. I want to answer; who has a question?: Yahoo! answers recommender system. In KDD, pages 1109--1117, 2011.

Digital Library

[6]

T. Fawcett. An introduction to roc analysis. Pattern Recognition Letters, 27(8):861--874, June 2006.

Digital Library

[7]

D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. AC M, 35:61--70, December 1992.

Digital Library

[8]

C.-F. Hsu, E. Khabiri, and J. Caverlee. Ranking comments on the social web. In Proceedings of the 2009 International Conference on Computational Science and Engineering-Volume 04, pages 90--97, August 2009.

Digital Library

[9]

M. Hu, A. Sun, and E.-P. Lim. Comments-oriented document summarization: understanding documents with readers AZ feedback. In Proc. of ACM SIGIR '08, pages 291--298, July 2008.

Digital Library

[10]

Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.

Digital Library

[11]

B. Li, S. Xu, and J. Zhang. Enhancing clustering blog documents by utilizing author/reader comments. In Proceedings of the 45th Annual Southeast Regional Conference, 2007, Winston-Salem, North Carolina, pages 94--99, March 2007.

Digital Library

[12]

Q. Li, J. Wang, Y. P. Chen, and Z. Lin. User comments for news recommendation in forum-based social media. Information Sciences, 180(24):4929--4939, December 2010.

Digital Library

[13]

S. Li and S. Manandhar. Improving question recommendation by exploiting information need. In ACL, pages 1425--1434, 2011.

Digital Library

[14]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, 2003.

Digital Library

[15]

C. D. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008.

[16]

G. Mishne and N. Glance. Leave a reply: An analysis of weblog comments. In Proceedings of the 15th international conference on World Wide Web, pages 625--632, May 2006.

[17]

B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2):1--135, 2008.

Digital Library

[18]

S. Park, M. Ko, J. Kim, Y. Liu, and J. Song. The politics of comments: Predicting political orientation of news stories with commenters sentiment patterns. In Proceedings of ACM Conference on Computer Supported Cooperative Work (CSCW) 2011, pages 113--122, March 2011.

Digital Library

[19]

P. A. Pavlou and A. Dimoka. The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4):392--414, December 2006.

Digital Library

[20]

I. Pilaszy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proc. of the 3rd ACM Conf. on Recommender Systems (RecSys'09), page 93100, New York, NY, USA, October 23--25 2009. ACM.

Digital Library

[21]

H. Rangwala and S. Jamali. Co-participation networks using comment information. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pages 315--318, May 2010.

[22]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proc. 25th Conference Annual Conference on Uncertainty in Artificial Intel ligence (UAI), pages 452--461, 2009.

Digital Library

[23]

F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.

[24]

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW '01, pages 285--295, New York, NY, USA, 2001. ACM.

Digital Library

[25]

A. Schuth, M. Marx, and M. de Rijke. Extracting the discussion structure in comments on news-articles. In WIDM, pages 97--104, November 2007.

Digital Library

[26]

S. Siersdorfer, S. Chelaru, W. Nejdl, and J. S. Pedro. How useful are your comments? analyzing and predicting youtube comments and comment ratings. In Proceedings of the 19th international conference on World Wide Web, pages 891--900, April 2010.

Digital Library

[27]

G. TakRScs, I. Pilaszy, B. NRZmeth, and D. Tikk. On the gravity recommendation system. In KDD C up Workshop at SIGKDD '07, pages 22--30, August 2007.

[28]

M. Tsagkias, W. Weerkamp, and M. de Rijke. Predicting the volume of comments on online news stories. In Proceeding of the 18th ACM conference on Information a nd knowledge management (CIKM), pages 1765--1768, November 2009.

Digital Library

[29]

M. Tsagkias, W. Weerkamp, and M. de Rijke. News comments: Exploring, modeling, and online prediction. InAd vances in Information Retrieval, 32nd European Conference on IR Research, ECIR 2010. Proceedings, pages 191--203, March 2010.

Digital Library

[30]

J. Weston, S. Bengio, and N. Usunier. Large scale image annotation: Learning to rank with joint word-image embeddings. Machine Learning Journal, 81:21--35, 2010.

Digital Library

[31]

W. G. Yee, A. Yates, S. Liu, and O. Frieder. Are web user comments useful for search? In 7th Workshop on Large-Scale Distributed Systems for Information Retrieval (LSDS-IR) at SIGIR '09, pages 63--70, July 2009.

Cited By

Steimann JMauve M(2024)Developing Custom-Made Comment-Recommendation Prototypes with a Modular Design FrameworkSocial Computing and Social Media10.1007/978-3-031-61281-7_7(97-112)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-61281-7_7
Qiu GSong CJiang LGuo Y(2022)Multi-view hybrid recommendation model based on deep learningIntelligent Data Analysis10.3233/IDA-21598826:4(977-992)Online publication date: 11-Jul-2022
https://doi.org/10.3233/IDA-215988
Qiu GGuo YSong C(2022)TANN: Text-based Attention Neural Network Recommendation Model2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)10.1109/BigDataSecurityHPSCIDS54978.2022.00031(119-124)Online publication date: May-2022
https://doi.org/10.1109/BigDataSecurityHPSCIDS54978.2022.00031
Show More Cited By

Index Terms

Care to comment?: recommendations for commenting on news stories
1. Applied computing
  1. Law, social and behavioral sciences

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Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 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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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Cited By

Steimann JMauve M(2024)Developing Custom-Made Comment-Recommendation Prototypes with a Modular Design FrameworkSocial Computing and Social Media10.1007/978-3-031-61281-7_7(97-112)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-61281-7_7
Qiu GSong CJiang LGuo Y(2022)Multi-view hybrid recommendation model based on deep learningIntelligent Data Analysis10.3233/IDA-21598826:4(977-992)Online publication date: 11-Jul-2022
https://doi.org/10.3233/IDA-215988
Qiu GGuo YSong C(2022)TANN: Text-based Attention Neural Network Recommendation Model2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)10.1109/BigDataSecurityHPSCIDS54978.2022.00031(119-124)Online publication date: May-2022
https://doi.org/10.1109/BigDataSecurityHPSCIDS54978.2022.00031
Liu BZhang HKong LNiu D(2021)Factorizing Historical User Actions for Next-Day Purchase PredictionACM Transactions on the Web10.1145/346822716:1(1-26)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3468227
Zhou YLiu LLee KPalanisamy BZhang Q(2020)Improving Collaborative Filtering with Social Influence over Heterogeneous Information NetworksACM Transactions on Internet Technology10.1145/339750520:4(1-29)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3397505
Risch JKrestel Rd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)A Dataset of Journalists' Interactions with Their ReadershipProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412764(3117-3124)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412764
Carmel DFyodorov YKuzi SMejer ARaiber FRainshmidt E(2019)Enriching News Articles with Related Search QueriesThe World Wide Web Conference10.1145/3308558.3313588(162-172)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313588
Zhou YLiu LZhang QLee KPalanisamy B(2019)Enhancing Collaborative Filtering with Multi-label ClassificationComputational Data and Social Networks10.1007/978-3-030-34980-6_35(323-338)Online publication date: 11-Nov-2019
https://doi.org/10.1007/978-3-030-34980-6_35
Mullick AGhosh SDutt RGhosh AChakraborty A(2019)Public Sphere 2.0: Targeted Commenting in Online News MediaAdvances in Information Retrieval10.1007/978-3-030-15719-7_23(180-187)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-3-030-15719-7_23
Meng YChen GLi JZhang SPera SEkstrand MAmatriain XO'Donovan J(2018)PsrecProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240390(397-401)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240390
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