A Method to Manage the Difference of Precision between Profiles and Items for Recommender System - Applied Upon a News Recommender System using SVM Approach

David Werner, Christophe Cruz

Abstract

Contractors, commercial and business decision-makers need economical information to drive their decisions. The production and distribution of a press review about French regional economic actors represents a prospecting tool on partners and competitors for the businessman. Our goal is to propose a customized review for each user, thus reducing the overload of useless information. Some systems for recommending news items already exist. The usefulness of external knowledge to improve the process has already been explained in information retrieval. The system’s knowledge base includes the domain knowledge used during the recommendation process. Our recommender system architecture is standard, but during the indexing task, the representations of content of each article and interests of users’ profiles created are based on this domain knowledge. Articles and Profiles are semantically defined in the Knowledge base via concepts, instances and relations. This paper deals with the similarity measure, a critical subtask in recommendation systems. The Vector Space Model is a well-known model used for relevance ranking. The problematic exposed here is the utilization of the standard VSM method with our indexing method.

References

  1. Billsus, D., Pazzani, M.J., 1999. A Personal News Agent that Talks, Learns and Explains. In: The Third Annual Conference on Autonomous Agents, ACM, pp. 268- 275.
  2. Ahn, J., Brusilovsky, P., Grady, J., He, D., Syn, S.Y., 2007. Open User Profiles for Adaptive News Systems: Help or Harm? In: 16th international conference on World Wide Web, ACM, pp. 11-20
  3. Cunningham, H., 2002 GATE, A General Architecture for Text Engineering. Computers and the Humanities 36 pp. 223-254
  4. Ehrig, M., Haase, P., Stojanovic, N., Hefke, M., 2005 Similarity for Ontologies A Comprehensive Framework. ECIS. Regensburg, Germany.
  5. Fellbaum, C., ed., 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA
  6. Gabrilovich, E., Dumais, S., Horvitz, E., 2004. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In WWW, pp. 482- 490.
  7. Getahun, F., Tekli, J., Richard, C., Viviani, M., Yetongnon, K., 2009. Relating RSS News/Items. In: 9th International Conference on Web Engineering, Springer, pp. 442-452
  8. IJntema, W., Goossen, F., Frasincar, F., Hogenboom, F., 2010 Ontology-Based News Recommendation. In: International Workshop on Business intelligence and the Web, BEWEB, EDBT/ICDT Workshops. pp. 16:1 16:6. ACM, New York, USA
  9. Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B., 2011. SCENE: A scalable two-stage personalized news recommendation system. In Proc. the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, Jul. 25-29, 2011, pp.124-134.
  10. Liu, J., Dolan, P., Pedersen, E.R., 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31-40. ACM.
  11. Middleton, S.E., Shadbolt, N.R., Roure, D.C.D., 2004. Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems 22, pp. 54-88
  12. Nageswara Rao, K., and Talwar, V.G., 2008. Application Domain and Functional Classification of Recommender Systems A Survey, Journal of Library & Information Technology, Vol. 28, No. 3: 17-35.
  13. Piskorski, J., Tanev, H., Wennerberg, P.O., 2007. Extracting Violent Events From OnLine News for Ontology Population. In: 10th International Conference on Business Information Systems, BIS. Lecture Notes in Computer Science, vol. 4439, pp. 287-300. Springer-Verlag Berlin Heidelberg.
  14. Popov, B., Kiryakov, A., Kirilov, A., Manov, D., Ognyanoff, D., Goranov, M., 2003. KIM Semantic Annotation Platform.
  15. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, North Carolina, United States. ACM Press, New York, pp. 175-186.
  16. Salton, G., 1970. The SMART retrieval system : Experiments in automatic document processing. Prentice Hall.
  17. Stumme, G., Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Sure, Y., Volz, R., Zacharias, V., 2003. The Karlsruhe view on ontologies. Technical report, University of Karlsruhe, Institute AIFB.
  18. Tanev, H., Piskorski, J., Atkinson, M., 2008. Real-Time News Event Extraction for Global Crisis Monitoring. In: 13th International Conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems, NLDB. Lecture Notes in Computer Science, vol. 5039, pp. 207-218. Springer-Verlag Berlin Heidelberg
  19. Voorhees, E., 1994. Query Expansion using LexicalSemantic Relations.
  20. Werner, D., Cruz, C., Nicolle, C., 2012. Ontology-based Recommender system of economic articles; In Proceedings of the 2012 Webist Conference, Porto, Portugal.
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Paper Citation


in Harvard Style

Werner D. and Cruz C. (2013). A Method to Manage the Difference of Precision between Profiles and Items for Recommender System - Applied Upon a News Recommender System using SVM Approach . In Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8565-54-9, pages 465-470. DOI: 10.5220/0004373404650470


in Bibtex Style

@conference{webist13,
author={David Werner and Christophe Cruz},
title={A Method to Manage the Difference of Precision between Profiles and Items for Recommender System - Applied Upon a News Recommender System using SVM Approach},
booktitle={Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2013},
pages={465-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004373404650470},
isbn={978-989-8565-54-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - A Method to Manage the Difference of Precision between Profiles and Items for Recommender System - Applied Upon a News Recommender System using SVM Approach
SN - 978-989-8565-54-9
AU - Werner D.
AU - Cruz C.
PY - 2013
SP - 465
EP - 470
DO - 10.5220/0004373404650470