Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets

The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness

Desarrollo de algoritmos basados en filtrado adaptativo y su aplicación en el estudio de la fonética acústica española

La hipótesis en la que se basa el desarrollo de esta tesis, se centra en la suposición de que partiendo del método de predicción lineal, es posible idear algoritmos de tratamiento de señal que permitan obtener una buena estimación de características espectrales significativas de la voz, especialmente en la detección de los formantes que se producen en el habla. Estos algoritmos ayudarían a construir un catálogo analítico de los principales sonidos del español, con el objetivo de complementar los estudios realizados hasta el momento en el campo de la fonética acústica. Parte de la complejidad que presenta esta tesis doctoral, viene dada por Ja naturaleza multidisciplinar de las materias que aborda. La correcta determinación de diversas características espectrales del habla, requiere un amplio conocimiento de los fundamentos del tratamiento de la señal de voz y de la fonética del idioma escogido. También resulta necesario poseer nociones adecuadas de todas las áreas relacionadas con el tratamiento de Ja voz, con el fin de enfocar los estudios partiendo de una visión global del campo seleccionado. Las investigaciones desarrolladas en este trabajo se han dividido en dos bloques fundamentales: tratamiento de señal y fonética acústica. En el apartado de. tratamiento de señal, se ha validado la hipótesis inicial. La obtención de los formantes del habla se ha basado en el método de predicción lineal, haciéndose una búsqueda de polos fuera de la zona habitual (el círculo unidad). La decisión de trabajar con funciones espectrales suavizadas ha resultado muy adecuada para la estimación de los formantes de voz. Partiendo de estas funciones espectrales se han ideado diferentes etapas que van detectando y resaltando los formantes del habla haciendo uso de transformaciones no lineales basadas en métodos algorítmicos. En el bloque reservado para las investigaciones en fonética acústica española, se aportan mapas tridimensionales de sonidos vocálicos que sirven como modelo para la extensión de las frecuentes clasificaciones bidimensionales que se utilizan en las publicaciones especializadas de fonética acústica. El empleo de una tercera dimensión permite complementar la información tradicional usada en las representaciones vocálicas. Así mismo se aportan trabajos que estudian Ja evolución de Jos formantes en situaciones de coarticulación. Estos trabajos se pueden considerar como una referencia innovadora para el desarrollo de investigaciones más elaboradas que se basen en Jos métodos y herramientas originales empleados en la tesis. En esta tesis se ofrece abundante y variado material en forma de espectros típicos, generalización de la evolución de los formantes, planos de situación de vocales, etc. Estos datos y resultados, junto a la metodología y herramientas informáticas empleados, pueden servir de base para la creación de aplicaciones que actúen sobre distintas áreas del tratamiento de la voz, tales como la enseñanza asistida de idiomas, logopedia, reconocimiento y síntesis del habla, detección de discapacidades, modelizaciones acústicas basadas en la fonética, etc.---ABSTRACT---This thesis is based on the following hypothesis: using the linear prediction method, it is possible to devise signal processing algorithms which obtain a good estimation of significant spectral characteristics of the voice, specially the formants of the speech. These algorithms would help to obtain an analytical catalogue of the main sounds of the Spanish Language, and therefore complement the current studies in the acoustic/phonetics area. Most of the complexity of this doctoral thesis comes from the different subjects covered by the speech processing area. The correct determination of diverse spectral characteristics in the speech, requires a deep knowledge in speech signal processing and the phonetics of the chosen language. In addition, it is necessary to incorporate a suitable background of all the subjects closely connected with speech processing. The research carried out in this work has been classified in two main areas: signal processing and acoustic phonetics. In the signal processing field, the initial hypothesis has been validated. The linear prediction method has been used to get the speech formants, searching the poles outside the usual zone (the unit circle). Working with smoothed spectral functions has been very suitable to fix the speech formants. Starting from these spectral functions, different stages have been developed in order to detect and emphasize the speech formants using nonlinear transformations based on algorithmic methods. With respect to the acoustic phonetics of Spanish, three-dimensional maps of vocalic sounds have been obtained. These maps can serve as a model to extend the two-dimensional classifications used in specialized publications of acoustic phonetics. The third dimension allows to complement the traditional information used in the vocalic representations. The formant evolution in "vowel-consonant-vowel" situations has been studied too. This work may be considered as a reference for future research based on the original methods and tools developed. Finally, abundant and varied material is offered in form of typical time-frequency representations, formant evolutions, two-dimensional and three-dimensional maps of vowels, etc. These data and results, the methodology, and the computing tools developed, can serve as a base to create applications related with different speech processing areas, such as computer assisted language learning, recognition and speech synthesis, acoustic modeling based on the phonetics, etc

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups

Técnicas del juicio oral en Colombia: retos y desafíos desde el aula de clase

A partir del primero de enero de 2005 y de forma gradual hasta el 31 de diciembre de 2008 entrará en vigencia un sistema penal de juzgamiento que varía radicalmente el hoy existente, al punto que « ... el grueso de los abogados quedará absolutamente derogado en sus conocimientos ... ». Sin embargo, · no es el mero tránsito legislativo el que explica tan preocupante pronóstico. Son las dificultades propias de pasar de un sistema prevalentemente escritural a uno fundado en la oralidad de las actuaciones