Advertisement

Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter

  • Owen Phelan
  • Kevin McCarthy
  • Mike Bennett
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)

Abstract

User-generated content has dominated the web’s recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitter’s 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowledge. We describe a social news service called Buzzer that is capable of adapting to the conversations that are taking place on Twitter to ranking personal RSS subscriptions. This is achieved by a content-based approach of mining trending terms from both the public Twitter timeline and from the timeline of tweets published by a user’s own Twitter friend subscriptions. We also present results of a live-user evaluation which demonstrates how these ranking strategies can add better item filtering and discovery value to conventional recency-based RSS ranking techniques.

Keywords

Recommender System News Story Twitter User Ranking Strategy Social Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Recommender system based on consumer product reviews. In: WI 2006: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, Washington, DC, USA, pp. 719–723. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  2. 2.
    Balabanovic, M., Shoham, Y.: Combining content-based and collaborative recommendation. Communications of the ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  3. 3.
    Brusilovsky, P., Henze, N.: Open corpus adaptive educational hypermedia. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 671–696. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Cantador, I., Bellogín, A., Castells, P.: News@hand: A Semantic Web Approach to Recommending News. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 279–283. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW 2007, pp. 271–280. ACM, New York (2007)Google Scholar
  6. 6.
    Esparza, S.G., O’Mahony, M.P., Smyth, B.: On the real-time web as a source of recommendation knowledge. In: RecSys 2010, Barcelona, Spain, September 26-30. ACM, New York (2010)Google Scholar
  7. 7.
    Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. SSRN eLibrary (2008)Google Scholar
  8. 8.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Procedings of the Joint 9th WEBKDD and 1st SNA-KDD Workshop, pp. 56–65 (2007)Google Scholar
  9. 9.
    Kamba, T., Bharat, K., Albers, M.C.: The krakatoa chronicle - an interactive, personalized, newspaper on the web. In: Proceedings of the Fourth International World Wide Web Conference, pp. 159–170 (1995)Google Scholar
  10. 10.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: WOSP 2008: Proceedings of the First Workshop on Online Social Networks, pp. 19–24. ACM, NY (2008)CrossRefGoogle Scholar
  11. 11.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW 2010, pp. 591–600 (2010)Google Scholar
  12. 12.
    Lerman, K.: Social Networks and Social Information Filtering on Digg. Arxiv preprint cs.HC/0612046 (2006)Google Scholar
  13. 13.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth National Conference on Artificial Intelligence, pp. 187–192 (2002)Google Scholar
  14. 14.
    Pazzani, M., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341 (2007)Google Scholar
  15. 15.
    Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: RecSys 2009: Proceedings of the Third ACM Conference on Recommender Systems, pp. 385–388. ACM, New York (2009)Google Scholar
  16. 16.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)CrossRefGoogle Scholar
  17. 17.
    Sen, S., Vig, J., Riedl, J.: Tagommenders: connecting users to items through tags. In: WWW 2009: Proceedings of the 18th International Conference on World Wide Web, pp. 671–680. ACM, New York (2009)Google Scholar
  18. 18.
    Wietsma, R.T.A., Ricci, F.: Product reviews in mobile decision aid systems. In: PERMID, Munich, Germany, pp. 15–18 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Owen Phelan
    • 1
  • Kevin McCarthy
    • 1
  • Mike Bennett
    • 1
  • Barry Smyth
    • 1
  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science & InformaticsUniversity College DublinIreland

Personalised recommendations