Music recommender systems. Proof of concept

Data overload is a well-known problem due to the availability of big on-line distributed databases. While providing a wealth of information the difficulties to find the sought data and the necessary time spent in the search call for technological solutions. Classical search engines alleviate this problem and at the same time have transformed the way people access to the information they are interested in. On the other hand, Internet also has changed the music consuming habits around the world. It is possible to find almost every recorded song or music piece. Over the last years music streaming platforms like Spotify, Apple Music or Amazon Music have contributed to a substantial change of users’ listening habits and the way music is commercialized and distributed. On-demand music platforms offer their users a huge catalogue so they can do a quick search and listen what they want or build up their personal library. In this context Music Recommender Systems may help users to discover music that match their tastes. Therefore music recommender systems are a powerful tool to make the most of an immense catalogue, impossible to be fully known by a human. This project aims at testing different music recommendation approaches applied to the particular case of users playlists. Several recommender alternatives were designed and evaluated: collaborative filtering systems, content-based systems and hybrid recommender systems that combine both techniques. Two systems are proposed. One system is content-based and uses correlation between tracks characterized by high-level descriptors and the other is an hybrid recommender that first apply a collaborative method to filter the database and then computes the final recommendation using Gaussian Mixture Models. Recommendations were evaluated using objective metrics and human evaluations, obtaining positive results.Ingeniería de Sistemas Audiovisuale

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm

Universal Schema for Knowledge Representation from Text and Structured Data

In data integration we transform information from a source into a target schema. A general problem in this task is loss of fidelity and coverage: the source expresses more knowledge than that can be fit into the target schema, or knowledge that is hard to fit into any schema at all. This problem is taken to an extreme in information extraction (IE) where the source is natural language---one of the most expressive forms of knowledge representation. To address this issue, one can either automatically learn a latent schema emergent in text (a brittle and ill-defined task), or manually define schemas. We propose instead to store data in a probabilistic representation of universal schema. This schema is simply the union of all source schemas, and we learn how to predict the cells of each source relation in this union. For example, we could store Freebase relations and relations that are expressed by natural language surface patterns. To populate such a database of universal schema, we present matrix factorization models that learn latent embedding vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach on New York Times and Freebase data. Besides binary relations, we use universal schema for unary relations, i.e., entity types. We explore various facets of universal schema matrix factorization models on a large-scale web corpus, including implicature among the relations. We evaluate our approach on the task of question answering using features obtained from universal schema, achieving state-of-the-art accuracy on a benchmark dataset

De la Información al Conocimiento. Aplicaciones basadas en implicaciones y computación paralela.

Sistemas de Recomendación Conversacionales Abordar la generación de recomendaciones haciendo uso de FCA es una aproximación existente en la literatura desde hace años. En esta tesis se ha abordado el problema de la dimensionalidad de SRs haciendo uso de conjuntos de implicaciones. Fecha de lectura de Tesis Doctoral: 29 Enero de 2019La gestión de la información es uno de los pilares esenciales de la Ingeniería Informática. Esta tesis doctoral toma como principal base teórica el Análisis Formal de Conceptos (FCA, por sus siglas en inglés: Formal Concept Analysis), y más concretamente, una de sus herramientas fundamentales: los conjuntos de implicaciones. La gestión inteligente de estos elementos mediante técnicas lógicas y computacionales confiere una alternativa para superar obstáculos en campos de la Ingeniería Informática como las bases de datos y los sistemas de recomendación (SRs). FCA parte de una representación de conjuntos de objetos y atributos por medio de tablas de datos. A partir de ahí, se generan dos herramientas básicas para representar el conocimiento: los retículos de conceptos y los conjuntos de implicaciones. Trabajar con implicaciones permite utilizar técnicas de razonamiento automático basadas en la lógica por medio de sistemas axiomáticos correctos y completos, como los axiomas de Armstrong y la Lógica de Simplificación. Estos métodos se utilizan en esta tesis doctoral sobre tres áreas de investigación: Claves Minimales Una clave de un esquema relacional está compuesta por un subconjunto de atributos que identifican a cada uno de los elementos de una relación. En concreto, se ha diseñado un nuevo método, denominado Closure Keys, que incorpora un mecanismo eficiente de poda de atributos e implicaciones mediante el método del Cierre de la Lógica de Simplificación. Generadores Minimales Se han estudiado, diseñado e implementado los métodos de generadores minimales referentes en la literatura y se ha hecho una clasificación de las ventajas e inconvenientes obtenidos por cada uno de ellos

Learning task-specific similarity

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 139-147).The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example-based learning methods. Similarity is commonly defined in terms of a conventional distance function, but such a definition does not necessarily capture the inherent meaning of similarity, which tends to depend on the underlying task. We develop an algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples. Our learning algorithm constructs, in a greedy fashion, an encoding of the data. This encoding can be seen as an embedding into a space, where a weighted Hamming distance is correlated with the unknown similarity. This allows us to predict when two previously unseen examples are similar and, importantly, to efficiently search a very large database for examples similar to a query. This approach is tested on a set of standard machine learning benchmark problems. The model of similarity learned with our algorithm provides and improvement over standard example-based classification and regression. We also apply this framework to problems in computer vision: articulated pose estimation of humans from single images, articulated tracking in video, and matching image regions subject to generic visual similarity.by Gregory Shakhnarovich.Ph.D

Learning and Decision Making in Social Contexts: Neural and Computational Models

Social interaction is one of humanity's defining features. Through it, we develop ideas, express emotions, and form relationships. In this thesis, we explore the topic of social cognition by building biologically-plausible computational models of learning and decision making. Our goal is to develop mechanistic explanations for how the brain performs a variety of social tasks, to test those theories by simulating neural networks, and to validate our models by comparing to human and animal data. We begin by introducing social cognition from functional and anatomical perspectives, then present the Neural Engineering Framework, which we use throughout the thesis to specify functional brain models. Over the course of four chapters, we investigate many aspects of social cognition using these models. We begin by studying fear conditioning using an anatomically accurate model of the amygdala. We validate this model by comparing the response properties of our simulated neurons with real amygdala neurons, showing that simulated behavior is consistent with animal data, and exploring how simulated fear generalization relates to normal and anxious humans. Next, we show that biologically-detailed networks may realize cognitive operations that are essential for social cognition. We validate this approach by constructing a working memory network from multi-compartment cells and conductance-based synapses, then show that its mnemonic performance is comparable to animals performing a delayed match-to-sample task. In the next chapter, we study decision making and the tradeoffs between speed and accuracy: our network gathers information from the environment and tracks the value of choice alternatives, making a decision once certain criteria are met. We apply this model to a two-choice decision task, fit model parameters to recreate the behavior of individual humans, and reproduce the speed-accuracy tradeoff evident in the human population. Finally, we combine our networks for learning, working memory, and decision making into a cognitive agent that uses reinforcement learning to play a simple social game. We compare this model with two other cognitive architectures and with human data from an experiment we ran, and show that our three cognitive agents recreate important patterns in the human data, especially those related to social value orientation and cooperative behavior. Our concluding chapter summarizes our contributions to the field of social cognition and proposes directions for further research. The main contribution of this thesis is the demonstration that a diverse set of social cognitive abilities may be explained, simulated, and validated using a functionally-descriptive, biologically-plausible theoretical framework. Our models lay a foundation for studying increasingly-sophisticated forms of social cognition in future work