Publicado 2012-06-15
Palabras clave
- Recuperación de Información.,
- contexto de usuario,
- filtrado colaborativo,
- expansión de consulta,
- búsqueda web
Cómo citar
Resumen
A pesar del continuo desarrollo que han tenido los buscadores Web modernos, estos aún no satisfacen a cabalidad las necesidades de los usuarios, siendo la relevancia de los documentos recuperados uno de los principales aspectos que afectan la calidad de búsqueda. En este artículo se propone un modelo de meta buscador Web que integra el filtrado colaborativo (basado en ítems) con la propuesta de Massimo Melucci, que se basa en proyectores sobre planos que se originan en la información del contexto del usuario. El modelo fue implementado en un meta buscador Web que recupera documentos de buscadores tradicionales como Google y Bing, donde se muestran los resultados por medio de una lista de documentos ordenados por relevancia, basado en la información del contexto del usuario y en la retroalimentación colaborativa de la comunidad. El modelo propuesto se constituye en un aporte para el área de recuperación de información, dado que muestra promisorios resultados en pruebas realizadas sobre colecciones cerradas y con usuarios.
Descargas
Referencias
- R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., 1999, p. 513.
- C. Manning, P. Raghavan, and H. Schütze, “An Introduction to Information Retrieval.” Cambridge University Press, Cambridge, England, 2007.
- C. J. V. Rijsbergen, Information Retrieval. Butterworth-Heinemann, 1979, p.208.
- M. Melucci, “Exploring a mechanics for contextaware information retrieval,” In Proceedings of the AAAI Spring Symposium on Quantum Interaction. AAAI Press, 2007.
- M. Melucci, “A basis for information retrieval in context,” ACM Transactions on Information Systems (TOIS), vol. 26, no. 3, pp. 1–41, 2008.
- J. Nielsen, “When search engines become answer engines,” Jakob Nielsen’s Alertbox, pp. 1–5, 2004.
- K. O’hara and N. Shabdolt, “Knowledge Technologies and the semantic web,” 2004. [Online]. Available: http://eprints.ecs.soton. ac.uk/12469/.
- D. Sullivan, “Nielsen NetRatings search engine ratings,” Search Engine Watch, 2006.
- R. Baeza-Yates, C. Castillo, and B. Keith, “Web Searching,” in Encyclopedia of Language & Linguistics, Oxford: Elsevier, 2006, pp. 527–538.
- M. Melucci, “Context modeling and discovery using vector space bases,” In Proceedings of the AAAI Spring Symposium on Quantum Interaction. AAAI Press, pp. 808–815, 2005.
- S. Liaw and H. Huang, “Information retrieval from the World Wide Web: a user-focused approach based on individual experience with search engines,” Computers in human behavior, vol. 22, no. 3, pp. 501–517, 2006.
- Y. Liu and C. Li, “A query expansion algorithm based on phrases semantic similarity,” Information Processing (ISIP), …, 2008.
- J. Rocchio, “Relevance feedback in information retrieval,” Englewood Cliffs, NJ: Prentice Hall., pp. 313–323, 1971.
- Y. Liu, C. Li, P. Zhang, and Z. Xiong, “A query expansion algorithm based on phrases semantic similarity,” Proceedings of the 2008 International Symposiums on Information Processing, 2008.
- S. Robertson and K. Jones, “Relevance weighting of search terms,” in Document retrieval systems, Taylor Graham Publishing, 1988, pp. 143–160.
- E. Garcia, “RSJ-PM Tutorial: A Tutorial on the Robertson-Sparck Jones Probabilistic Model for Information Retrieval,” 2009.
- E. N. Efthimiadis, “Query Expansion,” In: Martha E. Williams (ed.), Annual Review of Information Systems and Technology (ARIST), vol. 31, pp. 121–187.
- I. G. Kalmanovich and O. Kurland, “Cluster-based query expansion,” Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp. 646–647, 2009.
- A. Abdelali, J. Cowie, and H. S. Soliman, “Improving query precision using semantic expansion,” Inf. Process. Manage., vol. 43, no. 3, pp. 705–716, 2007.
- Claudio Biancalana and Alessandro Micarelli, “Social tagging in query expansion: A new way for personalized web search,” Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04, 2009.
- M. Bertier and R. Guerraoui, “Toward personalized query expansion,” Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, 2009.
- Z. D. and W. Liqing, “Study on Key Techniques of Query Expansion based on Ontology and Its Application,” in Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, 2009.
- T. Nguyen and T. Phan, “An ontology-based approach of query expansion,” Proceedings of the 9th International …, 2007.
- N. A. Segura, “An empirical analysis of ontologybased query expansion for learning resource searches using MERLOT and the Gene ontology,” Knowledge-Based Systems, vol. 24, no. 1, pp. 119– 133, Feb. 2011.
- L. Han and G. Chen, “HQE: A hybrid method for query expansion,” Expert Systems with Applications, vol. 36, pp. 7985–7991, 2009.
- M. Rahman, “A query expansion framework in image retrieval domain based on local and global analysis,” Information Processing & Management, vol. 47, no. 5, pp. 676–691, 2011.
- L. Jong-Seok and O. Sigurdur, “Two-way cooperative prediction for collaborative filtering recommendations,” Expert Systems with Applications, vol. 36, no. 3, pp. 5353–5361, 2009.
- Amazon, “Sitio web de Amazon.” [Online]. Available: http://www.amazon.com/.
- G. Linden, “Amazon. com recommendations: Item-to-item collaborative filtering,” Internet Computing, IEEE, vol. 7, no. 1, pp. 76–80, 2003.
- B. Sarwar and G. Karypis, “Item-based collaborative filtering recommendation algorithms,” Proceedings of the 10th international conference on World Wide Web, 2001.
- B. Marlin, “Collaborative filtering: A machine learning perspective,” University of Toronto, 2004.
- V. Schickel-Zuber, “Ontology filtering,” ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE, Suisse, 2007.
- J. A. Konstan, J. Riedl, A. Borchers, and J. L. Herlocker, “Recommender systems: A grouplens perspective,” in Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-00-04), 1998, pp. 60–64.
- P. Heymann, G. Koutrika, and H. GarciaMolina, “Can social bookmarking improve web search?,” Proceedings of the international conference on Web search and web data mining, 2008.
- C. Cobos, E. Estevez, M. Mendoza, L. Gomez, and E. León, “Algoritmos de expansión de consulta basados en una nueva función discreta de relevancia,” Revista UIS Ingenierías, vol. 10, no. 1, pp. 9–22, 2012.