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.
This work is gratefully supported by Science Foundation Ireland under Grant No. 07/CE/11147 CLARITY CSET.
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References
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)
Balabanovic, M., Shoham, Y.: Combining content-based and collaborative recommendation. Communications of the ACM 40, 66–72 (1997)
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)
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)
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)
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)
Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. SSRN eLibrary (2008)
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)
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)
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)
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)
Lerman, K.: Social Networks and Social Information Filtering on Digg. Arxiv preprint cs.HC/0612046 (2006)
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)
Pazzani, M., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341 (2007)
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)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
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)
Wietsma, R.T.A., Ricci, F.: Product reviews in mobile decision aid systems. In: PERMID, Munich, Germany, pp. 15–18 (2005)
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Phelan, O., McCarthy, K., Bennett, M., Smyth, B. (2011). Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_44
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DOI: https://doi.org/10.1007/978-3-642-20161-5_44
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