A Media Tracking and News Recommendation System

Servet Tasci, Ilyas Cicekli

2014

Abstract

Nowadays, the amount of documents on internet resources is increasing at an unprecedented speed and users are tired of searching important and related ones among enormous amount of documents. Users require a personalized support in sifting through large amounts of available information according to their interests and recommendation systems try to answer this need. In this context, it is crucial to offer user friendly tools that facilitate faster and more accurate access to articles in digital newspapers. In this paper, a time-based recommendation system for news domain is presented. News articles are recommended according to user dynamic and static profiles. User dynamic profiles reflect user past interests and recent interests play much bigger roles in the selection of recommendations. Our recommendation system is a complete content-based recommendation system together with categorization, summarization and news collection modules.

References

  1. Akin A.A., Akin, M.D. 2007. Zemberek, an open source NLP framework for Turkic languages. Available at http://zemberek.googlecode.com/.
  2. Asanov, D., 2011. Algorithms and Methods in Recommender Systems. Berlin Institute of Technology, http://www.snet.tuberlin.
  3. Balabanovic, M., Shoham, Y. 1997. Fab: Content-based, Collaborative Recommendation. Communications of the ACM 40(3), pp:66-72.
  4. Bennett, J.. Lanning S. 2007. The netflix prize. In Proceedings of KKDD cup and workshop, p. 35.
  5. Billsus, D., Pazzani, M. 1999. A hybrid user model for news story classification. In Proceedings of the Seventh International Conference on User Modeling. Banff, Canada, pp. 99-108.
  6. Cantador, I., Bellogín, A., Castells, P.. 2008. OntologyBased 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.
  7. O'Conner, M., Herlocker, J. 1999. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA.
  8. Davis, J., Goadrich, M. 2006. The relationship between precision recall and roc curves. In Proceedings of the 23rd international conference on machine learning (ICML).
  9. Fisher, M. J., Fieldsend, J.E., Everson R. M. 2004. Precision and recall optimisation for information access tasks. In first workshop on roc analysis in AL. European conference on artificial intelligence (ECAI'2004), Valencia, Spain.
  10. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A. 2007. User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, Vol. 4321, pp. 54-89. Springer, Heidelberg.
  11. Gong, S. 2010. A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering. Journal of Software, Vol. 5, No. 7, pp. 745-752.
  12. Goossen, F., IJntema, W. Frasincar, F., Hogenboom, F., Kaymak. U. 2011. News personalization using the CFIDF semantic recommender. In Proceedings of the International Conference on Web Intelligence, Mining and Semantics (WIMS 7811).
  13. Hahn, U., Mani, I. 2000. The challenges of automatic summarization. Computer, 33, pp. 29-36.
  14. Hovy, E., Lin, C-Y. 1999. Automated Text Summarization in SUMMARIST. I. Mani and M.T. Maybury (eds.), Advances in Automatic Text Summarization, The MIT Press, pp. 81-94.
  15. Kompan, M., Graudina, V. 2010. Content-based news recommendation. In Proceedings of the 11th Conference EC-WEB, pp. 61-72.
  16. Li, L., Chu, W., Langford, J., Schapire, R.E. A 2010. Contextual-Bandit Approach to Personalized News Article Recommendation. In Proceedings of the 19th international conference on World wide web, pp. 661- 670.
  17. Li, L., Li, T. 2013. News recommendation via hypergraph learning: encapsulation of user behavior and news content. In Proceedings of the sixth ACM international conference on Web search and data mining. pp. 305- 314
  18. Liang, T.-P., Lai, H-J. 2002. Discovering User Interests from Web Browser Behavior: An Application to Internet News Services. In Proceedings of 35th Annual Hawai'i International Conference on Systems Sciences. IEEE Computer Society Press.
  19. Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B. 2011. SCENE: A scalable two-stage personalized news recommendation system. In Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, pp.124-134.
  20. Liu, J., Dolan, P., Pedersen, E.R. 2010. Personalized News Recommendation Based on Click Behavior. In Proceedings of the 14th International Conference on Intelligent User Interfaces, Hong Kong, China.
  21. McLaughlin, M.R, Herlocker, J.L., 2004. A Collaborative Filtering Algorithm and Evaluation Metric that Accurately Model the User Experience, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, Sheffield, United Kingdom.
  22. Ozsoy, M.G., Cicekli, I., Alpaslan, F.N. 2011 Text Summarization using Latent Semantic Analysis, Journal of Information Science, Vol. 37, No. 4, pp:405-417.
  23. Radev, D.R., Fan, W., Zhang, Z. 2001. WebInessence : A Personalized Web-Based Multi-Document Summarization and Recommendation System, In Proceedings of the NAACL-01, pp. 79-88.
  24. Salton, G., McGill, M.J. 1986. Introduction to Modern Information Retrieval, McGraw-Hill, Inc, New York, NY.
  25. Saranya, K.G., Sadhasivam, G.S. 2012. A Personalized Online News Recommendation System. International Journal of Computer Applications, 57.
  26. Tan, A.-H., Toe. C. 1998. Learning user profiles for personalized information dissemination. In Proceedings of 1998 IEEE International Joint Conference on Neural Networks, Alaska, pp: 183-188.
  27. Zhou, T., Ren, J., Medo, M., Zhang, Y. 2007. Bipartite network projection and personal recommendation. Phys. Rev. E 76, 046115.
  28. Zhou, T., Zoltan, K., Liu, J., Medo, M., Wakeling, J. R., Zhang, Y. 2010. Solving the apparent diversityaccuracy dilemma of recommender systems. In Proceedings of the National Academy of Sciences, 107(10), 4511-4515.
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Paper Citation


in Harvard Style

Tasci S. and Cicekli I. (2014). A Media Tracking and News Recommendation System . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 53-60. DOI: 10.5220/0005072000530060


in Bibtex Style

@conference{kdir14,
author={Servet Tasci and Ilyas Cicekli},
title={A Media Tracking and News Recommendation System},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={53-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005072000530060},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - A Media Tracking and News Recommendation System
SN - 978-989-758-048-2
AU - Tasci S.
AU - Cicekli I.
PY - 2014
SP - 53
EP - 60
DO - 10.5220/0005072000530060