A RECOMMENDATION ALGORITHM FOR PERSONALIZED ONLINE NEWS BASED ON COLLECTIVE INTELLIGENCE AND CONTENT

Giovanni Giuffrida, Calogero G. Zarba

Abstract

We present a recommendation algorithm for online news based on collective intelligence and content. When a user asks for personalized news, our algorithm recommends news articles that (i) are popular among the members of the online community (the collective intelligence part), and (ii) are similar in content to the news articles the user has read in the past (the content part). Our algorithm computes its recomendations based on the collective behavior of the online users, as well as on the feedback the users provide to the algorithm’s recommendations. The users’ feedback can moreover be used to measure the effectiveness of our recomendation algorithm in terms of the information retrieval concepts of precision and recall. The cornerstone of our recommendation algorithm is a basic relevance algorithm that computes how relevant a news article is to a given user. This basic relevance algorithm can be optimized in order to obtain a faster online response at the cost of minimal offline computations. Moreover, it can be turned into an approximated algorithm for an even faster online response.

References

  1. Banos, E., Katakis, I., Bassiliades, N., Tsoumakas, G., and Vlahavas, I. P. (2006). PersoNews: A personalized news reader enhanced by machine learning and semantic filtering. In Ontologies, DataBases, and Applications of Semantics, pages 975-982.
  2. Bharat, K., Kamba, T., and Albers, M. (1998). Personalized, interactive news on the web. Multimedia Systems, 6(5):349-358.
  3. Borsje, J., Levering, L., and Frasincar, F. (2008). Hermes: A semantic web-based news decision support system. In Symposium on Applied Computing, pages 2415-2420.
  4. Das, A., Datar, M., Garg, A., and Rajaram, S. (2007). Google news personalization: Scalable online collaborative filtering. In International Conference on World Wide Web, pages 271-280.
  5. Lang, K. (1995). Newsweeder: Learning to filter netnews. In International Conference on Machine Learning, pages 331-339.
  6. Pazzani, M. J. and Billsus, D. (2007). Content-based recommendation systems. In The Adaptive Web, pages 325-341.
  7. Segaran, T. (2007). Programming Collective Intelligence: Building Smart Web 2.0 Applications. O'Reilly.
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Paper Citation


in Harvard Style

Giuffrida G. and G. Zarba C. (2011). A RECOMMENDATION ALGORITHM FOR PERSONALIZED ONLINE NEWS BASED ON COLLECTIVE INTELLIGENCE AND CONTENT . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 189-194. DOI: 10.5220/0003115401890194


in Bibtex Style

@conference{icaart11,
author={Giovanni Giuffrida and Calogero G. Zarba},
title={A RECOMMENDATION ALGORITHM FOR PERSONALIZED ONLINE NEWS BASED ON COLLECTIVE INTELLIGENCE AND CONTENT},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={189-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003115401890194},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A RECOMMENDATION ALGORITHM FOR PERSONALIZED ONLINE NEWS BASED ON COLLECTIVE INTELLIGENCE AND CONTENT
SN - 978-989-8425-40-9
AU - Giuffrida G.
AU - G. Zarba C.
PY - 2011
SP - 189
EP - 194
DO - 10.5220/0003115401890194