Information Retrieval Model with Query Expansion and User Preference Profile
Understanding the user's search intention enables identifying and extracting the most relevant and personalized search results from the available information, according to the user's needs. This paper proposes an algorithm for relevant information retrieval that combines user preferences p...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/14360
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15208
https://repositorio.uptc.edu.co/handle/001/14360
- Palabra clave:
- Personalized information retrieval
query expansion
user profile
semantic annotation
Recuperación de información personalizada
expansión de consulta
perfil de usuario
anotación semántica
- Rights
- License
- http://creativecommons.org/licenses/by/4.0
| Summary: | Understanding the user's search intention enables identifying and extracting the most relevant and personalized search results from the available information, according to the user's needs. This paper proposes an algorithm for relevant information retrieval that combines user preferences profile and query expansion to get relevant and personalized search results. The information retrieval process is validated using Precision, Recall and Mean Average Precision (MAP) metrics applied to a dataset that contains the standardized documents and preferences profiles. The results allowed us to demonstrate that the algorithm improves the information retrieval process by finding documents with better quality and greater relevance to the users' needs. |
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