Cagdas Esiyok
ABSTRACT
The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category.
- S. Agarwal. Learning to rank on graphs. Machine Learning, 81(3):333--357, 2010. Google Scholar
Digital Library
- D. Billsus and M. Pazzani. Adaptive news access. In The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, pages 550--570. Springer Berlin Heidelberg, 2007. Google ScholarDigital Library
- T. Bogers and A. van den Bosch. Comparing and evaluating information retrieval algorithms for news recommendation. In Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys '07, pages 141--144, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- I. Cantador, A. Bellogín, and P. Castells. News@hand: A semantic web approach to recommending news. In Proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH '08, pages 279--283, Berlin, Heidelberg, 2008. Springer-Verlag. Google ScholarDigital Library
- I. Cantador, A. Bellogín, and P. Castells. Ontology-based personalised and context-aware recommendations of news items. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01, WI-IAT '08, pages 562--565, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- M. Capelle, F. Hogenboom, A. Hogenboom, and F. Frasincar. Semantic news recommendation using wordnet and bing similarities. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13, pages 296--302, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- Y.-C. Chen, Y.-S. Lin, Y.-C. Shen, and S.-D. Lin. A modified random walk framework for handling negative ratings and generating explanations. ACM Trans. Intell. Syst. Technol., 4(1):12:1--12:21, Feb. 2013. Google ScholarDigital Library
- G. De Francisci Morales, A. Gionis, and C. Lucchese. From chatter to headlines: Harnessing the real-time web for personalized news recommendation. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, pages 153--162, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- F. Hopfgartner, B. Kille, A. Lommatzsch, T. Plumbaum, T. Brodt, and T. Heintz. Benchmarking news recommendations in a living lab. In CLEF'14: Proceedings of the Fifth International Conference of the CLEF Initiative. Springer Verlag, 09 2014. to appear.Google ScholarCross Ref
- B. Kille, F. Hopfgartner, T. Brodt, and T. Heintz. The plista dataset. In NRS, pages 16--23. ACM, 2013. Google ScholarDigital Library
- I. Paparrizos, B. B. Cambazoglu, and A. Gionis. Machine learned job recommendation. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 325--328, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- J.-W. Son, A.-Y. Kim, and S.-B. Park. A location-based news article recommendation with explicit localized semantic analysis. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '13, pages 293--302, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- M. Tavakolifard, J. A. Gulla, K. C. Almeroth, F. Hopfgartner, B. Kille, T. Plumbaum, A. Lommatzsch, T. Brodt, A. Bucko, and T. Heintz. Workshop and challenge on news recommender systems. In RecSys, RecSys '13, pages 481--482, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
Index Terms
- Users' reading habits in online news portals
Recommendations
Understanding User Attention and Engagement in Online News Reading
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data MiningPrior work on user engagement with online media identified web page dwell time as a key metric reflecting level of user engagement with online news articles. While on average, dwell time gives a reasonable estimate of user experience with a news article,...
Your Digital News Reading Habits Reflect Your Personality
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and PersonalizationThe way people read digital news - as distinct from what news they read - has emerged as a significant concern for research in user modelling and personalisation. Intuitively, some people read the news frequently and broadly whilst others read it ...
User Fatigue in Online News Recommendation
WWW '16: Proceedings of the 25th International Conference on World Wide WebMany aspects and properties of Recommender Systems have been well studied in the past decade, however, the impact of User Fatigue has been mostly ignored in the literature. User fatigue represents the phenomenon that a user quickly loses the interest on ...
Comments