Combining Disparate Information for Machine Learning.

This thesis considers information fusion for four different types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme -- the benefit to combining disparate information resulting in improved algorithm performance. In this dissertation, several new algorithms and applications to real-world datasets are presented. In Chapter II, a novel approach called Pareto Depth Analysis (PDA) is proposed for combining different dissimilarity metrics for anomaly detection. PDA is applied to video-based anomaly detection of pedestrian trajectories. Following a similar idea, in Chapter III we propose to use a similar Pareto Front method for a multiple-query information retrieval problem when different queries represent different semantic concepts. Pareto Front information retrieval is applied to multiple query image retrieval. In Chapter IV, we extend a recently proposed collaborative retrieval approach to incorporate complementary social network information, an approach we call Social Collaborative Retrieval (SCR). SCR is applied to a music recommendation system that combines both user history and friendship network information to improve recall and weighted recall performance. In Chapter V, we propose a framework that combines time series data at different time scales and offsets for more accurate estimation of multiple precision matrices. We propose a general fused graphical lasso approach to jointly estimate these precision matrices. The framework is applied to modeling financial time series data.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108878/1/coolmark_1.pd

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída 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

An overview of video recommender systems: state-of-the-art and research issues

Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation

The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Image Understanding by Socializing the Semantic Gap

Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models