skip to main content
10.1145/3109859.3109894acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

Recommending Personalized News in Short User Sessions

Published:27 August 2017Publication History

ABSTRACT

News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

References

  1. Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. Transactions on Knowledge and Data Engineering 17, 6 (2005), 734--749. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Amr Ahmed, Choon Hui Teo, S. V. N. Vishwanathan, and Alex Smola. 2012. Fair and Balanced: Learning to Present News Stories. In WSDM. 333--342. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. F. Bickenbach and E. Bode. 2003. Evaluating the Markov Property in Studies of Economic Convergence. International Regional Science Review 26, 3 (2003), 363--392.Google ScholarGoogle ScholarCross RefCross Ref
  4. Daniel Billsus and Michael J. Pazzani. 2007. Adaptive News Access. In The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Springer, Chapter 18, 550--570. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Michel Capelle, Alexander Hogenboom, Alexander Hogenboom, and Flavius Frasincar. 2013. Semantic News Recommendation Using WordNet and Bing Similarities Categories and Subject Descriptors. In Symposium on Applied Computing. 296--302. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google News Personalization: Scalable Online. In WWW. 271--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Gianmarco De Francisci Morales, Aristides Gionis, and Claudio Lucchese. 2012. From Chatter to Headlines: Harnessing the Real-timeWeb for Personalized News Recommendation. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM '12). ACM, New York, NY, USA, 153--162. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2015. Semantics-Aware Content-Based Recommender Systems. Springer US, Boston, MA, 119--159.Google ScholarGoogle Scholar
  9. Doychin Doychev, Aonghus Lawlor, Rachael Rafter, and Barry Smyth. 2014. An Analysis of Recommender Algorithms for Online News. In CLEF (Working Notes). 825--836.Google ScholarGoogle Scholar
  10. E. V. Epure, J. E. Ingvaldsen, R. Deneckere, and C. Salinesi. 2016. Process Mining for Recommender Strategies Support in News Media. In Proceedings of the 10th International Conference on Research Challenges in Information Science (RCIS). IEEE.Google ScholarGoogle Scholar
  11. Cagdas Esiyok, Benjamin Kille, Brijnesh-Johannes Jain, Frank Hopfgartner, and Sahin Albayrak. 2014. Users' Reading Habits in Online News Portals. In Proceedings of the 5th Information Interaction in Context Symposium (IIiX '14). ACM, New York, NY, USA, 263--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Anna Frankfort-Nachmias, ChavaLeon-Guerrero. 2006. Social Statistics for a Diverse Society. Pine Forge Press.Google ScholarGoogle Scholar
  13. Qi Gao, Fabian Abel, Geert-Jan Houben, and Ke Tao. 2011. Interweaving Trend and User Modeling for Personalized News Recommendation. In IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. IEEE, 100--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Florent Garcin, Kai Zhou, Boi Faltings, and Vincent Schickel. 2012. Personalized News Recommendation Based on Collaborative Filtering. In Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT '12). IEEE Computer Society, Washington, DC, USA, 437--441. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jon Atle Gulla, Bei Yu, Özlem Özgöbek, and Nafiseh Shabib. 2015. Third International Workshop on News Recommendation and Analytics (INRA 2015). In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 345--346. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Anat Hashavit, Roy Levin, Ido Guy, and Gilad Kutiel. 2016. Effective Trend Detection within a Dynamic Search Context. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 817--820. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based Recommendations with Recurrent Neural Networks. In Proceedings of the International Conference on Learning Representations.Google ScholarGoogle Scholar
  18. Wenxing Hong, Lei Li, and Tao Li. 2012. Product Recommendation with Temporal Dynamics. Expert systems with applications 39, 16 (2012), 12398--12406. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Amin Javari and Mahdi Jalili. 2015. A Probabilistic Model to Resolve Diversity - Accuracy Challenge of Recommendation Systems. Knowledge and Information Systems 44, 3 (2015), 609--627. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Nirmal Jonnalagedda, Susan Gauch, Kevin Labille, and Sultan Alfarhood. 2016. Incorporating Popularity in a Personalized News Recommender System. PeerJ Computer Science 2 (2016), e63.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. G. Kemény and J. L. Snell. 1960. Finite Markov Chains. Van Nostrand.Google ScholarGoogle Scholar
  22. Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. arXiv preprint arXiv:1606.08117 (2016).Google ScholarGoogle Scholar
  23. Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A Contextual-Bandit Approach to Personalized News Article Recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). ACM Press, New York, New York, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Lei Li, Li Zheng, Fan Yang, and Tao Li. 2014. Modeling and Broadening Temporal User Interest in Personalized News Recommendation. Expert Systems with Applications 41, 7 (2014), 3168--3177. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Ting-Peng Liang and Hung-Jen Lai. 2002. Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services. In HICSS. IEEE Computer Society, 203. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized News Recommendation Based on Click Behavior. In Proceedings of the International Conference on Intelligent User Interfaces. 31--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Andreas Lommatzsch. 2014. Real-Time News Recommendation Using Context- Aware Ensembles. In Proceedings of the 36th European Conference on IR Research (ECIR '14). Springer, 51--62.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Nic Newman. 2017. Overview and Key Findings of the 2016 Report. (2017). http://digitalnewsreport.org/survey/2016/overview-key-findings-2016/.Google ScholarGoogle Scholar
  29. Rasmus Kleis Nielsen. 2016. People Want Personalised Recommendations (Even as they Worry about the Consequences). In Digital News Report.Google ScholarGoogle Scholar
  30. Owen Phelan, Kevin McCarthy, and Barry Smyth. 2009. Using Twitter to Recommend Real-time Topical News. In Proceedings of the Third Conference on Recommender Systems (RecSys '09). ACM, New York, NY, USA, 385--388. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Nachiketa Sahoo, Param Vir Singh, and Tridas Mukhopadhyay. 2012. A Hidden Markov Model for Collaborative Filtering. MIS Q. 36, 4 (Dec. 2012), 1329--1356. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Guy Shani, David Heckerman, and Ronen I. Brafman. 2005. An MDP-Based Recommender System. Journal of Machine Learning research (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Mark Thompson. 2016. The Challenging New Economics of Journalism. In Digital News Report.Google ScholarGoogle Scholar
  34. Emily Thorson. 2008. Changing Patterns of News Consumption and Participation. Information, Communication & Society 11, 4 (2008), 473--489.Google ScholarGoogle ScholarCross RefCross Ref
  35. Giang Tran, Mohammad Alrifai, and Eelco Herder. 2015. Timeline summarization from relevant headlines. In European Conference on Information Retrieval. Springer International Publishing, 245--256.Google ScholarGoogle ScholarCross RefCross Ref
  36. David Vallet, Shlomo Berkovsky, Sebastien Ardon, Anirban Mahanti, and Mohamed Ali Kafaar. 2015. Characterizing and Predicting Viral-and-Popular Video Content. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 1591--1600. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. X. Wu, F. Xie, G. Wu, and W. Ding. 2011. Personalized News Filtering and Summarization on the Web. In Proceedings of the 23rd International Conference on Tools with Artificial Intelligence. IEEE, 414--421. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, and Yong Yu. 2012. Local Implicit Feedback Mining for Music Recommendation. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 91--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Qingyan Yang, Ju Fan, Jianyong Wang, and Lizhu Zhou. 2010. Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model. In Proceedings of the International Conference on Data Mining (ICDM '10). IEEE Computer Society, 1145--1150. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Recommending Personalized News in Short User Sessions

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems
              August 2017
              466 pages
              ISBN:9781450346528
              DOI:10.1145/3109859

              Copyright © 2017 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]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 27 August 2017

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              RecSys '17 Paper Acceptance Rate26of125submissions,21%Overall Acceptance Rate254of1,295submissions,20%

              Upcoming Conference

              RecSys '24
              18th ACM Conference on Recommender Systems
              October 14 - 18, 2024
              Bari , Italy

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader