12 research outputs found
Deep Learning for Recommender Systems
The widespread adoption of the Internet has led to an explosion in the number of choices available to consumers. Users begin to expect personalized content in modern E-commerce, entertainment and social media platforms. Recommender Systems (RS) provide a critical solution to this problem by maintaining user engagement and satisfaction with personalized content.
Traditional RS techniques are often linear limiting the expressivity required to model complex user-item interactions and require extensive handcrafted features from domain experts. Deep learning demonstrated significant breakthroughs in solving problems that have alluded the artificial intelligence community for many years advancing state-of-the-art results in domains such as computer vision and natural language processing.
The recommender domain consists of heterogeneous and semantically rich data such as unstructured text (e.g. product descriptions), categorical attributes (e.g. genre of a movie), and user-item feedback (e.g. purchases). Deep learning can automatically capture the intricate structure of user preferences by encoding learned feature representations from high dimensional data.
In this thesis, we explore five novel applications of deep learning-based techniques to address top-n recommendation. First, we propose Collaborative Memory Network, which unifies the strengths of the latent factor model and neighborhood-based methods inspired by Memory Networks to address collaborative filtering with implicit feedback. Second, we propose Neural Semantic Personalized Ranking, a novel probabilistic generative modeling approach to integrate deep neural network with pairwise ranking for the item cold-start problem. Third, we propose Attentive Contextual Denoising Autoencoder augmented with a context-driven attention mechanism to integrate arbitrary user and item attributes. Fourth, we propose a flexible encoder-decoder architecture called Neural Citation Network, embodying a powerful max time delay neural network encoder augmented with an attention mechanism and author networks to address context-aware citation recommendation. Finally, we propose a generic framework to perform conversational movie recommendations which leverages transfer learning to infer user preferences from natural language. Comprehensive experiments validate the effectiveness of all five proposed models against competitive baseline methods and demonstrate the successful adaptation of deep learning-based techniques to the recommendation domain
Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation
Tesis Doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications
throughout the world, the amount of data stored on the Web has been
growing exponentially. In this new digital era, a large number of companies
have emerged with the purpose of ltering the information available on the
web and provide users with interesting items. The algorithms and models
used to recommend these items are called Recommender Systems. These
systems are applied to a large number of domains, from music, books, or
movies to dating or Point-of-Interest (POI), which is an increasingly popular
domain where users receive recommendations of di erent places when
they arrive to a city.
In this thesis, we focus on exploiting the use of contextual information, especially
temporal and sequential data, and apply it in novel ways in both
traditional and Point-of-Interest recommendation. We believe that this type
of information can be used not only for creating new recommendation models
but also for developing new metrics for analyzing the quality of these
recommendations. In one of our rst contributions we propose di erent
metrics, some of them derived from previously existing frameworks, using
this contextual information. Besides, we also propose an intuitive algorithm
that is able to provide recommendations to a target user by exploiting the
last common interactions with other similar users of the system.
At the same time, we conduct a comprehensive review of the algorithms
that have been proposed in the area of POI recommendation between 2011
and 2019, identifying the common characteristics and methodologies used.
Once this classi cation of the algorithms proposed to date is completed, we
design a mechanism to recommend complete routes (not only independent
POIs) to users, making use of reranking techniques. In addition, due to the
great di culty of making recommendations in the POI domain, we propose
the use of data aggregation techniques to use information from di erent
cities to generate POI recommendations in a given target city.
In the experimental work we present our approaches on di erent datasets
belonging to both classical and POI recommendation. The results obtained
in these experiments con rm the usefulness of our recommendation proposals,
in terms of ranking accuracy and other dimensions like novelty, diversity,
and coverage, and the appropriateness of our metrics for analyzing temporal
information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones
en todo el mundo, la cantidad de datos almacenados en la red ha crecido
exponencialmente. En esta nueva era digital, han surgido un gran n umero
de empresas con el objetivo de ltrar la informaci on disponible en la red
y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos
utilizados para recomendar estos art culos reciben el nombre de Sistemas de
Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios,
desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs,
en ingl es), un dominio cada vez m as popular en el que los usuarios reciben
recomendaciones de diferentes lugares cuando llegan a una ciudad.
En esta tesis, nos centramos en explotar el uso de la informaci on contextual,
especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa
tanto en la recomendaci on cl asica como en la recomendaci on de POIs.
Creemos que este tipo de informaci on puede utilizarse no s olo para crear
nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas
m etricas para analizar la calidad de estas recomendaciones. En una de
nuestras primeras contribuciones proponemos diferentes m etricas, algunas
derivadas de formulaciones previamente existentes, utilizando esta informaci
on contextual. Adem as, proponemos un algoritmo intuitivo que es
capaz de proporcionar recomendaciones a un usuario objetivo explotando
las ultimas interacciones comunes con otros usuarios similares del sistema.
Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que
se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y
2019, identi cando las caracter sticas comunes y las metodolog as utilizadas.
Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la
fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo
POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking.
Adem as, debido a la gran di cultad de realizar recomendaciones en el
ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos
para utilizar la informaci on de diferentes ciudades y generar recomendaciones
de POIs en una determinada ciudad objetivo.
En el trabajo experimental presentamos nuestros m etodos en diferentes
conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los
resultados obtenidos en estos experimentos con rman la utilidad de nuestras
propuestas de recomendaci on en t erminos de precisi on de ranking y de
otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de
apropiadas son nuestras m etricas para analizar la informaci on temporal y
los sesgos en las recomendaciones producida
Exploiting subsequence matching in Recommender Systems
Desde su surgimiento al inicio de la década de los 90, los sistemas de recomendación han
experimentado un crecimiento exponencial empleándose en cada vez más aplicaciones debido
a la utilidad que tienen para ayudar a los usuarios a elegir artÃculos en función de
sus gustos y necesidades. Actualmente son indispensables en un gran número de empresas
que ofrecen su servicio a través de Internet, el medio de intercambio de información más
importante que existe. Por esta razón, la continua innovación en estos sistemas resulta imprescindible
para poder efectuar recomendaciones que sean capaces de seguir sorprendiendo
a los usuarios y mejorar las ya existentes.
En este Trabajo Fin de Máster hemos realizado un estudio e investigación acerca del estado
actual de estos sistemas, prestando especial atención a los sistemas de filtrado colaborativo
basados en vecinos y los basados en contenido. No obstante, debido a las desventajas
que puede tener cada sistema por separado normalmente en aplicaciones reales se emplean
combinaciones de varios sistemas, creando recomendadores hÃbridos. Como apoyo a este
estudio, se propone como aspecto novedoso el uso del algoritmo de la subcadena común
más larga (LCS) para ser utilizada como medida de similitud entre usuarios, introduciendo
además, una técnica general y transparente para generar secuencias haciendo uso tanto de
información de contenido como de información colaborativa, pudiendo generar recomendadores
hÃbridos de manera sencilla. Complementando a estos nuevos recomendadores,
también detallamos otros parámetros auxiliares (confianza, preferencia, normalizaciones
y distintas ordenaciones) que tienen como fin mejorar el rendimiento de estos sistemas
basados en LCS.
Por otro lado, además de la definición de estos nuevos recomendadores, el trabajo
se complementa con resultados experimentales haciendo uso de tres conjuntos de datos
conocidos en el área: Movielens, Lastfm y MovieTweetings. Cada uno de ellos estará
orientado a explotar un aspecto especÃfico de la generación de secuencias. Los resultados
han sido obtenidos haciendo uso de métricas de ranking (Precisión, Recall, MAP o nDCG)
y de novedad y diversidad (_-nDCG, EPC, EPD, Aggregate diversity, EILD y Gini). Los
resultados han tenido como fin comparar el rendimiento de los recomendadores basados en
la subsecuencia común más larga frente a otros recomendadores conocidos en el área.
Como resumen, se ha observado que los recomendadores propuestos resultan altamente
competitivos en las pruebas realizadas siendo incluso mejores en algunas ocasiones a otros
recomendadores conocidos en el área, tanto en términos de métricas de ranking como
de novedad y diversidad. No obstante, también se ha observado que, en algunos casos,
el uso de recomendadores hÃbridos basados en la subsecuencia común más larga obtiene
unos resultados peores que otras versiones puramente colaborativas. En cualquier caso,
consideramos que es una propuesta con potencial para seguir siendo investigada.Since their inception in the early 1990s, recommender systems have experienced exponential
growth as they are being used in a large number of applications because of their
usefulness in helping users choose items based on their tastes and needs. Nowadays, they
are indispensable in many companies that o er their service through the Internet, the
most important method for information exchange. For this reason, continuous innovation
in these systems is essential to make recommendations that are able to continue surprising
users, while improving the existing ones.
In this Master's Thesis, we have studied and researched on the current state of these
systems, paying special attention to collaborative ltering based on neighborhood and
content-based algorithms. However, due to the disadvantages that each system may have
separately, combinations of these systems are often used in real applications, creating hybrid
recommenders. To support this study, we propose the use of the longest common
subsequence (LCS) algorithm as a novel aspect to be used as a similarity metric between
users, also introducing a general and transparent technique to generate sequences using
both content and collaborative information, allowing us to generate hybrid recommenders
in a simple way. Complementing these new recommendations, we also detail other auxiliary
parameters (con dence, preference, normalization functions, and di erent orderings),
whose main goal is to improve the performance of these LCS-based systems.
On the other hand, in addition to the de nition of these new recommenders, the study
is complemented with experimental results using three well-known datasets in the area:
Movielens, Lastfm and MovieTweetings. Each one of them will be oriented to exploit a
speci c aspect of the sequence generation process. The results have been obtained by using
ranking metrics (Precision, Recall, MAP, or nDCG) and novelty and diversity metrics
( -nDCG, EPC, EPD, Aggregate diversity, EILD, and Gini). With these experiments,
we aimed at comparing the performance of recommenders based on the longest common
subsequence against other well-known recommenders in the area.
As a summary, we have observed in the experiments performed that the proposed
recommenders are highly competitive, and sometimes they are even better than other
recommenders known in the area, both in terms of ranking quality metrics, and novelty and
diversity dimensions. However, we have also observed that, in some cases, the use of hybrid
recommenders based on the longest common subsequence results in worse performance than
other purely collaborative versions. In any case, we believe this is a proposal with enough
potential to be worthy of further investigation
Learning by Fusing Heterogeneous Data
It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. This often gives rise to data that are represented in totally different input spaces. A basic premise behind the study of learning from heterogeneous data is that in many such cases, there exists some correspondence among certain input dimensions of different input spaces. In our work we found that a key bottleneck that prevents us from better understanding and truly fusing heterogeneous data at large scales is identifying the kind of knowledge that can be transferred between related data views, entities and tasks. We develop interesting and accurate data fusion methods for predictive modeling, which reduce or entirely eliminate some of the basic feature engineering steps that were needed in the past when inferring prediction models from disparate data. In addition, our work has a wide range of applications of which we focus on those from molecular and systems biology: it can help us predict gene functions, forecast pharmacological actions of small chemicals, prioritize genes for further studies, mine disease associations, detect drug toxicity and regress cancer patient survival data.
Another important aspect of our research is the study of latent factor models. We aim to design latent models with factorized parameters that simultaneously tackle multiple types of data heterogeneity, where data diversity spans across heterogeneous input spaces, multiple types of features, and a variety of related prediction tasks. Our algorithms are capable of retaining the relational structure of a data system during model inference, which turns out to be vital for good performance of data fusion in certain applications. Our recent work included the study of network inference from many potentially nonidentical data distributions and its application to cancer genomic data. We also model the epistasis, an important concept from genetics, and propose algorithms to efficiently find the ordering of genes in cellular pathways.
A central topic of our Thesis is also the analysis of large data compendia as predictions about certain phenomena, such as associations between diseases and involvement of genes in a certain phenotype, are only possible when dealing with lots of data. Among others, we analyze 30 heterogeneous data sets to assess drug toxicity and over 40 human gene association data collections, the largest number of data sets considered by a collective latent factor model up to date. We also make interesting observations about deciding which data should be considered for fusion and develop a generic approach that can estimate the sensitivities between different data sets
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
Q(sqrt(-3))-Integral Points on a Mordell Curve
We use an extension of quadratic Chabauty to number fields,recently developed by the author with Balakrishnan, Besser and M ̈uller,combined with a sieving technique, to determine the integral points overQ(√−3) on the Mordell curve y2 = x3 − 4