Term-Based Approach for Linking Digital News Stories

  • Muzammil Khan
  • Arif Ur Rahman
  • Muhammad Daud Awan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 806)

Abstract

The World Wide Web has become a platform for news publication in the past few years. Many television channels, magazines and newspapers have started publishing digital versions of the news stories online. It is observed that recommendation systems can automatically process lengthy articles and identify similar articles to readers based on a predefined criteria i.e. collaborative filtering, content-based filtering approach. The paper presents a content-based similarity measure for linking digital news stories published in various newspapers during the preservation process. The study compares similarity of news articles based on human judgment with a similarity value computed automatically using common ratio measure for stories. The results are generalized by defining a threshold value based on multiple experimental results using the proposed approach.

Keywords

Linking news stories Similarity measures Text processing 

References

  1. 1.
    Agarwal, D., Chen, B.-C., Elango, P., Wang, X.: Personalized click shaping through lagrangian duality for online recommendation. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 485–494. ACM (2012)Google Scholar
  2. 2.
    Athalye, S.: Recommendation system for news reader (2013)Google Scholar
  3. 3.
    Burda, D., Teuteberg, F.: Sustaining accessibility of information through digital preservation: a literature review. J. Inf. Sci. 39(4), 442–458 (2013)CrossRefGoogle Scholar
  4. 4.
    Chun, D.: On indexing of key words. Acta Editologica 16(2), 105–106 (2004)MathSciNetGoogle Scholar
  5. 5.
    da Silva, J.R., Ribeiro, C., Lopes, J.C.: A data curation experiment at U. Porto using DSpace (2011)Google Scholar
  6. 6.
    Doychev, D., Lawlor, A., Rafter, R., Smyth, B.: An analysis of recommender algorithms for online news. In: CLEF (Working Notes), pp. 825–836. Citeseer (2014)Google Scholar
  7. 7.
    Fortuna, B., Fortuna, C., Mladenić, D.: Real-time news recommender system. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 583–586. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15939-8_38 CrossRefGoogle Scholar
  8. 8.
    Khan, M., Ur Rahman, A.: Digital news story preservation framework. In: Proceedings of Digital Libraries: Providing Quality Information: 17th International Conference on Asia-Pacific Digital Libraries, ICADL 2015, Seoul, Korea, 9–12 December 2015, vol. 9469, p. 350. Springer (2015). https://doi.org/10.1007/978-3-319-27974-9
  9. 9.
    Khan, M., Ur Rahman, A., Daud Awan, M., Alam, S.M.: Normalizing digital news-stories for preservation. In: 2016 Eleventh International Conference on Digital Information Management (ICDIM), pp. 85–90. IEEE (2016)Google Scholar
  10. 10.
    Li, L., Li, T.: 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. ACM (2013)Google Scholar
  11. 11.
    Li, L., Wang, D.-D., Zhu, S.-Z., Li, T.: Personalized news recommendation: a review and an experimental investigation. J. Comput. Sci. Technol. 26(5), 754–766 (2011)CrossRefGoogle Scholar
  12. 12.
    Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3168–3177 (2014)CrossRefGoogle Scholar
  13. 13.
    Melville, P., Sindhwani, V.: Recommender systems. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 829–838. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-0-387-30164-8_705 Google Scholar
  14. 14.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157–164. ACM (2011)Google Scholar
  15. 15.
    Ur Rahman, A.: Data warehouses in the path from databases to archives. Ph.D. thesis, Faculty of Engineering, University of Porto, July 2013Google Scholar
  16. 16.
    Said, A., Bellogín, A., Lin, J., de Vries, A.: Do recommendations matter?: news recommendation in real life. In: Proceedings of the Companion Publication of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 237–240. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muzammil Khan
    • 1
  • Arif Ur Rahman
    • 2
  • Muhammad Daud Awan
    • 1
  1. 1.Department of Computer SciencePreston UniversityIslamabadPakistan
  2. 2.Department of Computer ScienceBahria UniversityIslamabadPakistan

Personalised recommendations