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

Mining interesting events on large and dynamic data

Nowadays, almost every human interaction produces some form of data. These data are available either to every user, e.g.~images uploaded on Flickr or to users with specific privileges, e.g.~transactions in a bank. The huge amount of these produced data can easily overwhelm humans that try to make sense out of it. The need for methods that will analyse the content of the produced data, identify emerging topics in it and present the topics to the users has emerged. In this work, we focus on emerging topics identification over large and dynamic data. More specifically, we analyse two types of data: data published in social networks like Twitter, Flickr etc.~and structured data stored in relational databases that are updated through continuous insertion queries. In social networks, users post text, images or videos and annotate each of them with a set of tags describing its content. We define sets of co-occurring tags to represent topics and track the correlations of co-occurring tags over time. We split the tags to multiple nodes and make each node responsible of computing the correlations of its assigned tags. We implemented our approach in Storm, a distributed processing engine, and conducted a user study to estimate the quality of our results. In structured data stored in relational databases, top-k group-by queries are defined and an emerging topic is considered to be a change in the top-k results. We maintain the top-k result sets in the presence of updates minimising the interaction with the underlying database. We implemented and experimentally tested our approach.Heutzutage entstehen durch fast jede menschliche Aktion und Interaktion Daten. Fotos werden auf Flickr bereitgestellt, Neuigkeiten über Twitter verbreitet und Kontakte in Linkedin und Facebook verwaltet; neben traditionellen Vorgängen wie Banktransaktionen oder Flugbuchungen, die Änderungen in Datenbanken erzeugen. Solch eine riesige Menge an Daten kann leicht überwältigend sein bei dem Versuch die Essenz dieser Daten zu extrahieren. Neue Methoden werden benötigt, um Inhalt der Daten zu analysieren, neu entstandene Themen zu identifizieren und die so gewonnenen Erkenntnisse dem Benutzer in einer übersichtlichen Art und Weise zu präsentieren. In dieser Arbeit werden Methoden zur Identifikation neuer Themen in großen und dynamischen Datenmengen behandelt. Dabei werden einerseits die veröffentlichten Daten aus sozialen Netzwerken wie Twitter und Flickr und andererseits strukturierte Daten aus relationalen Datenbanken, welche kontinuierlich aktualisiert werden, betrachtet. In sozialen Netzwerken stellen die Benutzer Texte, Bilder oder Videos online und beschreiben diese für andere Nutzer mit Schlagworten, sogenannten Tags. Wir interpretieren Gruppen von zusammen auftretenden Tags als eine Art Thema und verfolgen die Beziehung bzw. Korrelation dieser Tags über einen gewissen Zeitraum. Abrupte Anstiege in der Korrelation werden als Hinweis auf Trends aufgefasst. Die eigentlich Aufgabe, das Zählen von zusammen auftretenden Tags zur Berechnung von Korrelationsmaßen, wird dabei auf eine Vielzahl von Computerknoten verteilt. Die entwickelten Algorithmen wurden in Storm, einem neuartigen verteilten Datenstrommanagementsystem, implementiert und bzgl. Lastbalancierung und anfallender Netzwerklast sorgfältig evaluiert. Durch eine Benutzerstudie wird darüber hinaus gezeigt, dass die Qualität der gewonnenen Trends höher ist als die Qualität der Ergebnisse bestehender Systeme. In strukturierten Daten von relationalen Datenbanksystemen werden Beste-k Ergebnislisten durch Aggregationsanfragen in SQL definiert. Interessant dabei sind eintretende Änderungen in diesen Listen, was als Ereignisse (Trends) aufgefasst wird. In dieser Arbeit werden Methoden präsentiert diese Ergebnislisten möglichst effizient instand zu halten, um Interaktionen mit der eigentlichen Datenbank zu minimieren

Mining interesting events on large and dynamic data

Nowadays, almost every human interaction produces some form of data. These data are available either to every user, e.g.~images uploaded on Flickr or to users with specific privileges, e.g.~transactions in a bank. The huge amount of these produced data can easily overwhelm humans that try to make sense out of it. The need for methods that will analyse the content of the produced data, identify emerging topics in it and present the topics to the users has emerged. In this work, we focus on emerging topics identification over large and dynamic data. More specifically, we analyse two types of data: data published in social networks like Twitter, Flickr etc.~and structured data stored in relational databases that are updated through continuous insertion queries. In social networks, users post text, images or videos and annotate each of them with a set of tags describing its content. We define sets of co-occurring tags to represent topics and track the correlations of co-occurring tags over time. We split the tags to multiple nodes and make each node responsible of computing the correlations of its assigned tags. We implemented our approach in Storm, a distributed processing engine, and conducted a user study to estimate the quality of our results. In structured data stored in relational databases, top-k group-by queries are defined and an emerging topic is considered to be a change in the top-k results. We maintain the top-k result sets in the presence of updates minimising the interaction with the underlying database. We implemented and experimentally tested our approach.Heutzutage entstehen durch fast jede menschliche Aktion und Interaktion Daten. Fotos werden auf Flickr bereitgestellt, Neuigkeiten über Twitter verbreitet und Kontakte in Linkedin und Facebook verwaltet; neben traditionellen Vorgängen wie Banktransaktionen oder Flugbuchungen, die Änderungen in Datenbanken erzeugen. Solch eine riesige Menge an Daten kann leicht überwältigend sein bei dem Versuch die Essenz dieser Daten zu extrahieren. Neue Methoden werden benötigt, um Inhalt der Daten zu analysieren, neu entstandene Themen zu identifizieren und die so gewonnenen Erkenntnisse dem Benutzer in einer übersichtlichen Art und Weise zu präsentieren. In dieser Arbeit werden Methoden zur Identifikation neuer Themen in großen und dynamischen Datenmengen behandelt. Dabei werden einerseits die veröffentlichten Daten aus sozialen Netzwerken wie Twitter und Flickr und andererseits strukturierte Daten aus relationalen Datenbanken, welche kontinuierlich aktualisiert werden, betrachtet. In sozialen Netzwerken stellen die Benutzer Texte, Bilder oder Videos online und beschreiben diese für andere Nutzer mit Schlagworten, sogenannten Tags. Wir interpretieren Gruppen von zusammen auftretenden Tags als eine Art Thema und verfolgen die Beziehung bzw. Korrelation dieser Tags über einen gewissen Zeitraum. Abrupte Anstiege in der Korrelation werden als Hinweis auf Trends aufgefasst. Die eigentlich Aufgabe, das Zählen von zusammen auftretenden Tags zur Berechnung von Korrelationsmaßen, wird dabei auf eine Vielzahl von Computerknoten verteilt. Die entwickelten Algorithmen wurden in Storm, einem neuartigen verteilten Datenstrommanagementsystem, implementiert und bzgl. Lastbalancierung und anfallender Netzwerklast sorgfältig evaluiert. Durch eine Benutzerstudie wird darüber hinaus gezeigt, dass die Qualität der gewonnenen Trends höher ist als die Qualität der Ergebnisse bestehender Systeme. In strukturierten Daten von relationalen Datenbanksystemen werden Beste-k Ergebnislisten durch Aggregationsanfragen in SQL definiert. Interessant dabei sind eintretende Änderungen in diesen Listen, was als Ereignisse (Trends) aufgefasst wird. In dieser Arbeit werden Methoden präsentiert diese Ergebnislisten möglichst effizient instand zu halten, um Interaktionen mit der eigentlichen Datenbank zu minimieren

Usage-driven Maintenance of Knowledge Organization Systems

Knowledge Organization Systems (KOS) are typically used as background knowledge for document indexing in information retrieval. They have to be maintained and adapted constantly to reflect changes in the domain and the terminology. In this thesis, approaches are provided that support the maintenance of hierarchical knowledge organization systems, like thesauri, classifications, or taxonomies, by making information about the usage of KOS concepts available to the maintainer. The central contribution is the ICE-Map Visualization, a treemap-based visualization on top of a generalized statistical framework that is able to visualize almost arbitrary usage information. The proper selection of an existing KOS for available documents and the evaluation of a KOS for different indexing techniques by means of the ICE-Map Visualization is demonstrated. For the creation of a new KOS, an approach based on crowdsourcing is presented that uses feedback from Amazon Mechanical Turk to relate terms hierarchically. The extension of an existing KOS with new terms derived from the documents to be indexed is performed with a machine-learning approach that relates the terms to existing concepts in the hierarchy. The features are derived from text snippets in the result list of a web search engine. For the splitting of overpopulated concepts into new subconcepts, an interactive clustering approach is presented that is able to propose names for the new subconcepts. The implementation of a framework is described that integrates all approaches of this thesis and contains the reference implementation of the ICE-Map Visualization. It is extendable and supports the implementation of evaluation methods that build on other evaluations. Additionally, it supports the visualization of the results and the implementation of new visualizations. An important building block for practical applications is the simple linguistic indexer that is presented as minor contribution. It is knowledge-poor and works without any training. This thesis applies computer science approaches in the domain of information science. The introduction describes the foundations in information science; in the conclusion, the focus is set on the relevance for practical applications, especially regarding the handling of different qualities of KOSs due to automatic and semiautomatic maintenance

Detecting New, Informative Propositions in Social Media

The ever growing quantity of online text produced makes it increasingly challenging to find new important or useful information. This is especially so when topics of potential interest are not known a-priori, such as in “breaking news stories”. This thesis examines techniques for detecting the emergence of new, interesting information in Social Media. It sets the investigation in the context of a hypothetical knowledge discovery and acquisition system, and addresses two objectives. The first objective addressed is the detection of new topics. The second is filtering of non-informative text from Social Media. A rolling time-slicing approach is proposed for discovery, in which daily frequencies of nouns, named entities, and multiword expressions are compared to their expected daily frequencies, as estimated from previous days using a Poisson model. Trending features, those showing a significant surge in use, in Social Media are potentially interesting. Features that have not shown a similar recent surge in News are selected as indicative of new information. It is demonstrated that surges in nouns and news entities can be detected that predict corresponding surges in mainstream news. Co-occurring trending features are used to create clusters of potentially topic-related documents. Those formed from co-occurrences of named entities are shown to be the most topically coherent. Machine learning based filtering models are proposed for finding informative text in Social Media. News/Non-News and Dialogue Act models are explored using the News annotated Redites corpus of Twitter messages. A simple 5-act Dialogue scheme, used to annotate a small sample thereof, is presented. For both News/Non-News and Informative/Non-Informative classification tasks, using non-lexical message features produces more discriminative and robust classification models than using message terms alone. The combination of all investigated features yield the most accurate models

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

CACIC 2015 : XXI Congreso Argentino de Ciencias de la Computación. Libro de actas

Actas del XXI Congreso Argentino de Ciencias de la Computación (CACIC 2015), realizado en Sede UNNOBA Junín, del 5 al 9 de octubre de 2015.Red de Universidades con Carreras en Informática (RedUNCI