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The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance.2

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One of the central problems in the eld of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures { those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures { those that also depend on the class of users who examine the pattern. The focus of this paper is on studying subjective measures of interestingness. These measures are classi ed into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is de ned in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it a ects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.

Declaración de obra originalYo declaro lo siguiente:He leído el Acuerdo 035 de 2003 del Consejo Académico de la Universidad Nacional. «Reglamento sobre propiedad intelectual» y la Normatividad Nacional relacionada al respeto de los derechos de autor. Esta disertación representa mi trabajo original, excepto donde he reconocido las ideas, las palabras, o materiales de otros autores.Cuando se han presentado ideas o palabras de otros autores en esta disertación, he realizado su respectivo reconocimiento aplicando correctamente los esquemas de citas y referencias bibliográficas en el estilo requerido.He obtenido el permiso del autor o editor para incluir cualquier material con derechos de autor (por ejemplo, tablas, figuras, instrumentos de encuesta o grandes porciones de texto).Por último, he sometido esta disertación a la herramienta de integridad académica, definida por la universidad. ________________________________ JUAN FELIPE MUÑOZ FERNÁNDEZFecha 23/02/2021 Agradecimientos Al Ingeniero Juan Carlos Restrepo Carvajal por sus aportes en las discusiones alrededor de todos los temas que enmarca esta tesis.

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

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