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| Nombre | PhD. Thesis. Recommender System based on linked Data |
| Descripción | This doctoral thesis explored recommender systems (RS) that leverage Linked Data to improve recommendation generation from large and heterogeneous datasets available on the Web of Data. The project presented a state-of-the-art review of Linked Data-based recommendation frameworks and algorithms, developed the AlLied framework for implementing, testing, and comparing recommendation algorithms across different domains, and implemented graph-based and machine learning approaches for candidate generation, ranking, and grouping of resources. Additionally, the thesis proposed ReDyAl, a dynamic recommendation algorithm capable of selecting optimal strategies based on implicit relationships in Linked Data, and evaluated the accuracy and performance of these approaches through experimentation and real-world use cases. |
| Programa | Doctorado en Ingeniería Telemática |
| Estado tesis | Finalizada |
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Universidad del Cauca | Calle 5 No. 4-70 | Telefax (+57 2)823 2955 | fiet_tm@unicauca.edu.co
Popayán - Colombia