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Dynamic personalized recommendation of comment-eliciting stories

Published: 09 September 2012 Publication History

Abstract

Media Websites often solicit users' comments on content items such as videos, news stories, blog posts, etc. Commenting activity increases user engagement with the sites, by both comment writers and readers, and so sites are looking for ways to increase the volume of comments. This work develops a recommender system aiming to present users with items -- news stories, in our case -- on which they are likely to comment. We combine items' content with a collaborative-filtering approach (utilizing users' co-commenting patterns) in a latent factor modeling framework. Building upon previous work, we focus on a continuous, real-time approach to address the problem above. After an initial training period during which commenting activity of users is observed, the system is tested at each subsequent comment submission event by predicting which story is being commented on by a given user at a given time. Our results show that we are able to overcome the site's inherent presentation bias and outperform a strong baseline as users' commenting history grows.

References

[1]
R. Bandari, S. Asur, and B. A. Huberman. The pulse of news in social media: Forecasting popularity. CoRR, abs/1202.0332, 2012.
[2]
E. Shmueli, A. Kagian, Y. Koren, and R. Lempel. Care to comment? recommendations for commenting on news stories. In Proc. 21st International World Wide Web Conference (WWW'2012), April 2012.
[3]
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.

Cited By

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  • (2021)Personalized Thread Recommendation on Thai Internet ForumProceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence10.1145/3507548.3507589(267-272)Online publication date: 4-Dec-2021
  • (2020)HyCoNN: Hybrid Cooperative Neural Networks for Personalized News Discussion Recommendation2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00011(41-48)Online publication date: Dec-2020
  • (2020)Addressing the Item Cold-Start Problem by Attribute-Driven Active LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289153032:4(631-644)Online publication date: 1-Apr-2020
  • Show More Cited By

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      cover image ACM Conferences
      RecSys '12: Proceedings of the sixth ACM conference on Recommender systems
      September 2012
      376 pages
      ISBN:9781450312707
      DOI:10.1145/2365952
      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: 09 September 2012

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      Author Tags

      1. collaborative filtering
      2. personalization
      3. user comments

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      • Short-paper

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      RecSys '12
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      RecSys '12: Sixth ACM Conference on Recommender Systems
      September 9 - 13, 2012
      Dublin, Ireland

      Acceptance Rates

      RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;
      Overall Acceptance Rate 254 of 1,295 submissions, 20%

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      Cited By

      View all
      • (2021)Personalized Thread Recommendation on Thai Internet ForumProceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence10.1145/3507548.3507589(267-272)Online publication date: 4-Dec-2021
      • (2020)HyCoNN: Hybrid Cooperative Neural Networks for Personalized News Discussion Recommendation2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00011(41-48)Online publication date: Dec-2020
      • (2020)Addressing the Item Cold-Start Problem by Attribute-Driven Active LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289153032:4(631-644)Online publication date: 1-Apr-2020
      • (2019)Improving the Attribute-Based Active Learning by Clustering the New Items2019 IEEE World Congress on Services (SERVICES)10.1109/SERVICES.2019.00095(343-344)Online publication date: Jul-2019
      • (2015)ExcUseMeProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800183(83-90)Online publication date: 16-Sep-2015
      • (2015)Serving Ads to "Yahoo Answers" Occasional VisitorsProceedings of the 24th International Conference on World Wide Web10.1145/2740908.2741997(1257-1262)Online publication date: 18-May-2015
      • (2015)Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal DesignProceedings of the 24th International Conference on World Wide Web10.1145/2736277.2741109(45-54)Online publication date: 18-May-2015
      • (2014)Item cold-start recommendationsProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2645751(89-96)Online publication date: 6-Oct-2014
      • (2013)What should I comment: Recommending posts for commenting2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)10.1109/SOCPAR.2013.7054112(117-122)Online publication date: Dec-2013
      • (2012)Recommendation challenges in web media settingsProceedings of the sixth ACM conference on Recommender systems10.1145/2365952.2365992(205-206)Online publication date: 9-Sep-2012

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