Classification of authors for a recommendation process integrated to a scientific meta-search engine

The search for scientific production on the web has become a challenge, both in terms of volume, variety and updating speed. It requires tools that help the user to obtain relevant results when executing a query. Within these tools, this team has developed a specific meta-search engine for the area...

Full description

Autores:
Viloria, Amelec
Crissien Borrero, Tito José
Pineda, Omar
Pertuz, Luciana
Orellano, Nataly
Vargas Mercado, Carlos
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7742
Acceso en línea:
https://hdl.handle.net/11323/7742
https://doi.org/10.1007/978-981-15-4875-8_14
https://repositorio.cuc.edu.co/
Palabra clave:
Bibliometric indicators
Scientific data
Scientific authors
Classification scheme
Recommendation systems
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_17ba100b18cb2ef66a6512fcb4da344d
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7742
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Classification of authors for a recommendation process integrated to a scientific meta-search engine
title Classification of authors for a recommendation process integrated to a scientific meta-search engine
spellingShingle Classification of authors for a recommendation process integrated to a scientific meta-search engine
Bibliometric indicators
Scientific data
Scientific authors
Classification scheme
Recommendation systems
title_short Classification of authors for a recommendation process integrated to a scientific meta-search engine
title_full Classification of authors for a recommendation process integrated to a scientific meta-search engine
title_fullStr Classification of authors for a recommendation process integrated to a scientific meta-search engine
title_full_unstemmed Classification of authors for a recommendation process integrated to a scientific meta-search engine
title_sort Classification of authors for a recommendation process integrated to a scientific meta-search engine
dc.creator.fl_str_mv Viloria, Amelec
Crissien Borrero, Tito José
Pineda, Omar
Pertuz, Luciana
Orellano, Nataly
Vargas Mercado, Carlos
dc.contributor.author.spa.fl_str_mv Viloria, Amelec
Crissien Borrero, Tito José
Pineda, Omar
Pertuz, Luciana
Orellano, Nataly
Vargas Mercado, Carlos
dc.subject.spa.fl_str_mv Bibliometric indicators
Scientific data
Scientific authors
Classification scheme
Recommendation systems
topic Bibliometric indicators
Scientific data
Scientific authors
Classification scheme
Recommendation systems
description The search for scientific production on the web has become a challenge, both in terms of volume, variety and updating speed. It requires tools that help the user to obtain relevant results when executing a query. Within these tools, this team has developed a specific meta-search engine for the area of computer science. In its evolution, it is intended to include recommendations from authors for each of its users’ queries. The generation of such recommendations requires a method capable of classifying the authors in order to define their inclusion and position in a list of suggestions for the end-user. This paper presents a method that fulfills this objective, after being evaluated and having obtained results that allow to propose its inclusion in later development of the recommendation system.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-21T13:39:46Z
dc.date.available.none.fl_str_mv 2021-01-21T13:39:46Z
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https://doi.org/10.1007/978-981-15-4875-8_14
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dc.relation.references.spa.fl_str_mv 1. Schler, J., Koppel, M., Argamon, S., Pennebaker, J. W.: Effects of age and gender on blogging. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, March 27–29 (2006) pp 199–205
2. Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)
3. Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents, New York, USA, ACM (2011) pp. 37–44
4. Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”: A study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ICWSM 2013 (2013)
5. Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th Workshop on Natural Language Processing and Cognitive Science (NLPCS-2013) (2013)
6. Bedford, D.: Evaluating classification schema and classification decisions. Bull. Am. Soc. Inf. Sci. Technology 39, 13–21 (2013)
7. Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human Language Technology Conference (HLT-NAACL 2003) (2003)
8. Viloria, A., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Godoy, A. R. M., Moreno, G. C., Kamatkar, S. J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)
9. Tang, J.: AMiner: Mining deep knowledge from big scholar data. In: Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland pp. 373–373 (2016)
10. Obit, J. H., Ouelhadj, D., Landa-Silva, D., Vun, T. K., Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, https://doi.org/10.1109/his.2011.6122088 (2011)
11. Lewis, M. R. R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)
12. Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., Deng, Y.: An application of genetic algorithm for university course timetabling problem, pp. 2119–2122, https://doi.org/10.1109/ccdc.2011.5968555 (2011)
13. Mahiba, A.A., Durai, C.A.D.: Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng. 38, 253–263 (2012)
14. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)
15. C. & Sotelo-Figueroa, M. A., Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J.: Generic memetic algorithm for course timetabling. In: ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer, vol. 547, pp. 481–492 (2014)
16. Nguyen, K., Lu, T., Le, T., Tran, N.: Memetic algorithm for a university course timeta-bling problem. pp. 67–71.
17. Aladag, C., Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacet. J. Math. Stat., pp. 53–64 (2007)
18. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program (report 826) (1989)
19. McGrail, M.R., Rickard, C.M., Jones, R.: Publish or perish: a systematic review of interventions to increase academic publication rates. High. Educ. Res. Dev. 25, 19–35 (2006)
20. Costas, R., van Leeuwen, T.N., Bordons, M.: A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact. J. Am. Soc. Inf. Sci. 61, 1564–1581 (2010)
21. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. (Paul), Wang, K.: An overview of microsoft academic service (MAS) and applications. In: Proceedings of the 24th International Conference on World Wide Web. pp. 243–246. ACM, New York, USA (2015)
22. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J. P., Borrero, T. C., Varela, N. (2018, June). Web visibility profiles of Top 100 Latin American universities. In: International Conference on Data Mining and Big Data. pp. 254–262. Springer, Cham
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spelling Viloria, AmelecCrissien Borrero, Tito JoséPineda, OmarPertuz, LucianaOrellano, NatalyVargas Mercado, Carlos2021-01-21T13:39:46Z2021-01-21T13:39:46Z2020https://hdl.handle.net/11323/7742https://doi.org/10.1007/978-981-15-4875-8_14Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The search for scientific production on the web has become a challenge, both in terms of volume, variety and updating speed. It requires tools that help the user to obtain relevant results when executing a query. Within these tools, this team has developed a specific meta-search engine for the area of computer science. In its evolution, it is intended to include recommendations from authors for each of its users’ queries. The generation of such recommendations requires a method capable of classifying the authors in order to define their inclusion and position in a list of suggestions for the end-user. This paper presents a method that fulfills this objective, after being evaluated and having obtained results that allow to propose its inclusion in later development of the recommendation system.Viloria, AmelecCrissien Borrero, Tito José-will be generated-orcid-0000-0002-7459-7941-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Pertuz, LucianaOrellano, NatalyVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_14Bibliometric indicatorsScientific dataScientific authorsClassification schemeRecommendation systemsClassification of authors for a recommendation process integrated to a scientific meta-search engineArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Schler, J., Koppel, M., Argamon, S., Pennebaker, J. W.: Effects of age and gender on blogging. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, March 27–29 (2006) pp 199–2052. Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)3. Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents, New York, USA, ACM (2011) pp. 37–444. Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”: A study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ICWSM 2013 (2013)5. Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th Workshop on Natural Language Processing and Cognitive Science (NLPCS-2013) (2013)6. Bedford, D.: Evaluating classification schema and classification decisions. Bull. Am. Soc. Inf. Sci. Technology 39, 13–21 (2013)7. Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human Language Technology Conference (HLT-NAACL 2003) (2003)8. Viloria, A., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Godoy, A. R. M., Moreno, G. C., Kamatkar, S. J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)9. Tang, J.: AMiner: Mining deep knowledge from big scholar data. In: Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland pp. 373–373 (2016)10. Obit, J. H., Ouelhadj, D., Landa-Silva, D., Vun, T. K., Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, https://doi.org/10.1109/his.2011.6122088 (2011)11. Lewis, M. R. R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)12. Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., Deng, Y.: An application of genetic algorithm for university course timetabling problem, pp. 2119–2122, https://doi.org/10.1109/ccdc.2011.5968555 (2011)13. Mahiba, A.A., Durai, C.A.D.: Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng. 38, 253–263 (2012)14. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)15. C. & Sotelo-Figueroa, M. A., Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J.: Generic memetic algorithm for course timetabling. In: ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer, vol. 547, pp. 481–492 (2014)16. Nguyen, K., Lu, T., Le, T., Tran, N.: Memetic algorithm for a university course timeta-bling problem. pp. 67–71.17. Aladag, C., Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacet. J. Math. Stat., pp. 53–64 (2007)18. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program (report 826) (1989)19. McGrail, M.R., Rickard, C.M., Jones, R.: Publish or perish: a systematic review of interventions to increase academic publication rates. High. Educ. Res. Dev. 25, 19–35 (2006)20. Costas, R., van Leeuwen, T.N., Bordons, M.: A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact. J. Am. Soc. Inf. Sci. 61, 1564–1581 (2010)21. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. (Paul), Wang, K.: An overview of microsoft academic service (MAS) and applications. In: Proceedings of the 24th International Conference on World Wide Web. pp. 243–246. ACM, New York, USA (2015)22. Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J. P., Borrero, T. C., Varela, N. (2018, June). Web visibility profiles of Top 100 Latin American universities. In: International Conference on Data Mining and Big Data. pp. 254–262. 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