Técnicas de Big Data y Machine-Learning para Recomendador
Bibliográfico
| dc.contributor.advisor | Gregorio Rodríguez, Carlos | |
| dc.contributor.author | Gonzalo Fernández, Alejandro | |
| dc.date.accessioned | 2023-06-17T10:18:30Z | |
| dc.date.available | 2023-06-17T10:18:30Z | |
| dc.date.issued | 2020-07 | |
| dc.description | Calificación: 9,3 | |
| dc.description.abstract | En este Trabajo Fin de Máster se presenta la idea de mejorar los recomendadores bibliográficos. Por ello presentamos los distintos sistemas de recomendación en un primer capítulo, el procesamiento de lenguaje natural en un segundo y en el tercero y cuarto capítulo presentamos el problema y nuestra hipótesis de mejora junto con su implementación. La principal idea es crear un clasificador en diferentes temáticas: ciencia ficción, histórico, policíaco, etc. Esta clasificación servirá para realizar un esquema de un sistema de recomendación bibliográfico que proporciona recomendaciones basadas en los perfiles temáticos de los usuarios. Para solventar el problema del gran tamaño de estos datos usaremos la Ley de Zipf como pieza fundamental. | |
| dc.description.abstract | In this Master’s Project the idea of improving literary recommendations is presented. Different recommendation systems are discussed in the first chapter and the second chapter discusses natural language processing. In the third and fourth chapters, the problem is presented along with an improvement hypothesis and its implementation. The main idea is to create a classifier for different genres: science fiction, historical fiction, crime, etc. This classification will serve as an outline of a literary recommendation system that provides recommendations based on the thematic profiles of users. A solution based on Zipf’s Law was used to deal with the large dataset. | |
| dc.description.department | Sección Deptal. de Sistemas Informáticos y Computación | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.status | submitted | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/68580 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/9237 | |
| dc.language.iso | spa | |
| dc.master.title | Tratamiento estadístico computacional de la información | |
| dc.rights | Atribución-NoComercial-CompartirIgual 3.0 España | |
| dc.rights.accessRights | open access | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/3.0/es/ | |
| dc.subject.cdu | 004 | |
| dc.subject.cdu | 51 | |
| dc.subject.cdu | 519.22 | |
| dc.subject.keyword | Sistemas de Recomendación | |
| dc.subject.keyword | Big Data | |
| dc.subject.keyword | Machine Learning | |
| dc.subject.keyword | Procesamiento del Lenguaje Natural | |
| dc.subject.keyword | Ley de Zipf | |
| dc.subject.keyword | Recommendation Systems | |
| dc.subject.keyword | Natural Language Processing | |
| dc.subject.keyword | Zipf law | |
| dc.subject.ucm | Informática (Informática) | |
| dc.subject.ucm | Matemáticas (Matemáticas) | |
| dc.subject.ucm | Estadística | |
| dc.subject.unesco | 1203.17 Informática | |
| dc.subject.unesco | 12 Matemáticas | |
| dc.subject.unesco | 1209 Estadística | |
| dc.title | Técnicas de Big Data y Machine-Learning para Recomendador Bibliográfico | |
| dc.title.alternative | Big Data and Machine Learning Techniques for Recommendation Systems | |
| dc.type | master thesis | |
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| dspace.entity.type | Publication | |
| relation.isAdvisorOfPublication | 05a01c46-aac8-42b2-a6bc-4b95860cf5bf | |
| relation.isAdvisorOfPublication.latestForDiscovery | 05a01c46-aac8-42b2-a6bc-4b95860cf5bf |
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