Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Vol. 32 Núm. 64 (2023)
Abril-Junio 2023 (Publicación Continua)

Articulos

Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuarios

https://doi.org/10.19053/01211129.v32.n64.2023.15208

Publicado 2023-05-31

Hubert Viltres-Sala
Universidad de las Ciencias Informáticas

Vivian Estrada-Sentí
Universidad de las Ciencias Informáticas

Juan-Pedro Febles-Rodríguez
Universidad de las Ciencias Informáticas

Gerdys-Ernesto Jiménez-Moya
Universidad de las Ciencias Informáticas

Resumen

Comprender la intención de búsqueda del usuario permite identificar y extraer los resultados de búsqueda más relevantes y personalizados de la información disponible según sus necesidades. En el presente artículo se plantea un algoritmo para la recuperación de información relevante que combina las preferencias del perfil del usuario y la expansión de consulta para obtener resultados de búsqueda relevantes y personalizados. El proceso de recuperación de información se valida mediante las métricas de Precision, Recall y Mean Average Precision (MAP) aplicadas a un conjunto de datos que contiene los documentos estandarizados y los perfiles de preferencias. Los resultados permitieron demostrar que el algoritmo mejora el proceso de recuperación de información al arrojar documentos con mejor calidad y relevancia según las necesidades de los usuarios.

Palabras clave

Recuperación de información personalizada, expansión de consulta, perfil de usuario, anotación semántica

PDF (English) XML (English)

Citas

  1. H. Viltres, P. Leyva, J. P. Febles, V. Sentí, “Information retrieval with semantic annotation,” in 17th LACCEI International Multi-Conference for Engineering, Education, and Technology, 2019. https://doi.org/10.18687/LACCEI2019.1.1.308 DOI: https://doi.org/10.18687/LACCEI2019.1.1.308
  2. T. Rafa, S. Kechid, “Semantic Representation of a Geo-Social User Profile for a Personalised Information Retrieval,” Journal of Information and Knowledge Management, vol. 20, no. 4, e2150044, 2021. https://doi.org/10.1142/S0219649221500441 DOI: https://doi.org/10.1142/S0219649221500441
  3. P. P. Joby, “Expedient information retrieval system for web pages using the natural language modeling,” Journal of Artificial Intelligence, vol. 2, no. 2, pp. 100-110, 2020. https://doi.org/10.36548/jaicn.2020.2.003 DOI: https://doi.org/10.36548/jaicn.2020.2.003
  4. S. Sengan, G. K. Kamalam, J. Vellingiri, J. Gopal, P. Velayutham, V. Subramaniyaswamy, “Medical information retrieval systems for e-Health care records using fuzzy based machine learning model,” Microprocessors and Microsystems, In-Press, e103344, 2020. https://doi.org/10.1016/j.micpro.2020.103344 DOI: https://doi.org/10.1016/j.micpro.2020.103344
  5. V. Suma, “A novel information retrieval system for distributed cloud using hybrid deep fuzzy hashing algorithm,” JITDW, vol. 2, no. 3, pp. 151-160, 2020. https://doi.org/10.36548/jitdw.2020.3.003 DOI: https://doi.org/10.36548/jitdw.2020.3.003
  6. A. Jalilifard, V.F. Caridá, A. F. Mansano, R. S. Cristo, F. P. C. da Fonseca, “Semantic sensitive TF-IDF to determine word relevance in documents,” in Advances in Computing and Network Communications, 2021, pp. 327-337. https://doi.org/10.1007/978-981-33-6987-0_27 DOI: https://doi.org/10.1007/978-981-33-6987-0_27
  7. S. Zhuang, H. LI, G. Zuccon, “Deep query likelihood model for information retrieval,” in European Conference on Information Retrieval, 2021. pp. 463-470. https://doi.org/10.1007/978-3-030-72240-1_49 DOI: https://doi.org/10.1007/978-3-030-72240-1_49
  8. X. Liao, Z. Zhao, “Unsupervised approaches for textual semantic annotation, a survey,” ACM Computing Surveys, vol 52, no. 4, pp. 1-45, 2019. https://doi.org/10.1145/3324473 DOI: https://doi.org/10.1145/3324473
  9. D. Di Caprio, F. J. Santos-Arteaga, M. Tavana, “An information retrieval benchmarking model of satisficing and impatient users' behavior in online search environments,” Expert Systems with Applications, vol. 191, e116352, 2022. https://doi.org/10.1016/j.eswa.2021.116352 DOI: https://doi.org/10.1016/j.eswa.2021.116352
  10. S. Albukhitan, A. Alnazer, T. Helmy, “Framework of semantic annotation of Arabic document using deep learning,” Procedia Computer Science, vol. 170, pp. 989-994, 2020. https://doi.org/10.1016/j.procs.2020.03.096 DOI: https://doi.org/10.1016/j.procs.2020.03.096
  11. W. Wei, Q. Wu, D. Chen, Y. Zhang, W. Liu, G. Duan, X. Luo, “Automatic image annotation based on an improved nearest neighbor technique with tag semantic extension model,” Procedia Computer Science, vol. 183, pp. 616-623, 2021. https://doi.org/10.1016/j.procs.2021.02.105 DOI: https://doi.org/10.1016/j.procs.2021.02.105
  12. H. K. Azad, A. Deepak, “Query expansion techniques for information retrieval: a survey,” Information Processing and Management, vol. 56, no. 5, pp. 1698-1735, 2019. https://doi.org/10.1016/j.ipm.2019.05.009 DOI: https://doi.org/10.1016/j.ipm.2019.05.009
  13. S. Dahir, A. “El Qadi, A query expansion method based on topic modelling and DBpedia features,” International Journal of Information Management Data Insights, vol. 1, no. 2, e100043, 2021. https://doi.org/10.1016/j.jjimei.2021.100043 DOI: https://doi.org/10.1016/j.jjimei.2021.100043
  14. S. Jain, K. R. Seeja, R Jindal, “A fuzzy ontology framework in information retrieval using semantic query expansion,” International Journal of Information Management Data Insights, vol. 1, no. 1, e100009, 2021. https://doi.org/10.1016/j.jjimei.2021.100009 DOI: https://doi.org/10.1016/j.jjimei.2021.100009
  15. S. Malik, U. Shoaib, S. A. C. Bukhari, H. El Sayed, M. A. Khan, “A hybrid query expansion framework for the optimal retrieval of the biomedical literature,” Smart Health, vol. 23, e100247, 2022. https://doi.org/10.1016/j.smhl.2021.100247 DOI: https://doi.org/10.1016/j.smhl.2021.100247
  16. S. Abri, R. Abri, S. Çetin, “Group-based personalization using topical user profile,” in 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020, pp. 181-186. https://doi.org/10.1145/3386392.3399559 DOI: https://doi.org/10.1145/3386392.3399559
  17. Z. Ma, Z. Dou, Y. Zhu, H. Zhong, J. R Wen, “One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 555-564. https://doi.org/10.1145/3404835.3462828 DOI: https://doi.org/10.1145/3404835.3462828
  18. D. Zhou, X. Wu, W. Zhao, S. Lawless, J. Liu, “Query expansion with enriched user profiles for personalized search utilizing folksonomy data,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 7, pp. 1536-1548, 2017. https://doi.org/10.1109/TKDE.2017.2668419 DOI: https://doi.org/10.1109/TKDE.2017.2668419
  19. M. Bravo, A. Aldea, L. F. Hoyos-Reyes, “Automated Ontology Population and Enrichment of Scientific Publications,” Journal of Physics: Conference Series, vol. 1, e012139, 2021. https://doi.org/10.1088/1742-6596/1828/1/0121 DOI: https://doi.org/10.1088/1742-6596/1828/1/012139
  20. K. Gupta, N. Sachdeva, V. Pudi, “Explicit modelling of the implicit short term user preferences for music recommendation,” in European Conference on Information Retrieval, 2018. pp. 333-344. https://doi.org/10.1007/978-3-319-76941-7_25 DOI: https://doi.org/10.1007/978-3-319-76941-7_25
  21. J. Choudhary, D. S. Tomar, D. P. Singh, “An Efficient Hybrid User Profile Based Web Search Personalization Through Semantic Crawler,” National Academy Science Letters, vol. 42, no. 2, pp, 105-108, 2019. https://doi.org/10.1007/s40009-018-0686-2 DOI: https://doi.org/10.1007/s40009-018-0686-2
  22. F. Zarrinkalam, H. Fani, E. Bagheri, “Extracting, Mining and Predicting Users' Interests from Social Networks,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, pp. 1407-1408. https://doi.org/10.1145/3292500.3332279 DOI: https://doi.org/10.1145/3331184.3331383
  23. S. Gauch, M. Speretta, A. Chandramouli, A. Micarelli, “User profiles for personalized information access,” in The adaptive Web, 2007, pp. 54-89. https://doi.org/10.1007/978-3-540-72079-9_2 DOI: https://doi.org/10.1007/978-3-540-72079-9_2
  24. E. Vicente-López, L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, “Use of textual and conceptual profiles for personalized retrieval of political documents,” Knowledge-Based Systems, vol. 112, pp. 127-141, 2016. https://doi.org/10.1016/j.knosys.2016.09.005 DOI: https://doi.org/10.1016/j.knosys.2016.09.005
  25. A. K. Nandanwar, J. Choudhary, D. P. Singh, “Web search personalization based on the principle of the ant colony,” Procedia Computer Science, vol. 189, pp. 100-107, 2021. https://doi.org/10.1016/j.procs.2021.05.073 DOI: https://doi.org/10.1016/j.procs.2021.05.073
  26. F. T. da Silva, J. E. Maia, “Query Expansion in Text Information Retrieval with Local Context and Distributional Model,” Journal of Digital Information Management, vol. 17, no. 6, e313, 2019. https://10.6025/jdim/2019/17/6/313-320 DOI: https://doi.org/10.6025/jdim/2019/17/6/313-320
  27. M. Pereira, E. Etemad, F. Paulovich, “Iterative learning to rank from explicit relevance feedback,” in Proceedings of the 35th Annual ACM Symposium on Applied Computing, 2020, pp. 698-705. https://doi.org/10.1145/3341105.3374002 DOI: https://doi.org/10.1145/3341105.3374002
  28. J. Serrano-Guerrero, F. P. Romero, J. A. Olivas, “A relevance and quality-based ranking algorithm applied to evidence-based medicine,” Computer methods and programs in biomedicine, vol. 191, e105415, 2020. https://doi.org/10.1016/j.cmpb.2020.105415 DOI: https://doi.org/10.1016/j.cmpb.2020.105415
  29. J. Wang, M. Pan, T. He, X. Huang, X. Wang, X. Tu, “A Pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval,” Information Processing and Management, vol. 57, no. 6, e102342, 2020. https://doi.org/10.1016/j.ipm.2020.102342 DOI: https://doi.org/10.1016/j.ipm.2020.102342
  30. S. Neji, T. Chenaina, A. M. Shoeb, L. B. Ayed, “HIR: a hybrid IR ranking model,” in IEEE 45th Annual Computers, Software, and Applications Conference, 2021. pp. 1717-1722. https://10.1109/COMPSAC51774.2021.00256 DOI: https://doi.org/10.1109/COMPSAC51774.2021.00256
  31. B. Selvalakshmi, M. Subramaniam, “Intelligent ontology based semantic information retrieval using feature selection and classification,” Cluster Computing, vol. 22, no. 5, pp. 12871-12881, 2019. https://doi.org/10.1007/s10586-018-1789-8 DOI: https://doi.org/10.1007/s10586-018-1789-8
  32. G. J. Hahm, M. Y. Yi, J. H. Lee, H. W. Suh, “A personalized query expansion approach for engineering document retrieval,“ Advanced Engineering Informatics, vol. 28, no 4, pp. 344-359, 2014. https://doi.org/10.1016/j.aei.2014.04.002 DOI: https://doi.org/10.1016/j.aei.2014.04.002
  33. B. Xu, H. Lin, L. Yang, K. Xu, Y. Zhang, D. Zhang, Z. Yang, J. Wang, Y. Lin, F. Yin, “A supervised term ranking model for diversity enhanced biomedical information retrieval,” BMC bioinformatics, vol. 20, no 16, e590, 2019. https://doi.org/10.1186/s12859-019-3080-2 DOI: https://doi.org/10.1186/s12859-019-3080-2

Descargas

Los datos de descargas todavía no están disponibles.

Artículos similares

1 2 > >> 

También puede {advancedSearchLink} para este artículo.