Contact Recommendation: Effects on the Evolution of Social Networks

En los últimas dos décadas y media, el desarrollo y crecimiento de los sistemas de recomendación ha progresado cada vez más rápido. Esta expansión ha dado lugar a la confluencia entre las tecnologías de recomendación y otras áreas adyacentes, y, en particular, con las tecnologías de redes sociales, que han experimentado un crecimiento exponencial en los últimos años. El presente trabajo explora uno de los problemas más novedosos que surgen de la confluencia entre ambas áreas: la recomendación de contactos en redes sociales. Nuestro trabajo se centra, por un lado, en obtener una perspectiva completa de la efectividad de una amplia selección de algoritmos de recomendación, incluyendo algunas contribuciones originales, y considerando perspectivas novedosas que van más allá del acierto de la recomendación. Por otro, en el estudio de la influencia que los algoritmos de recomendación de contactos ejercen sobre la evolución de las redes sociales y sus propiedades. Una fracción no despreciable de los nuevos enlaces que aparecen en las modernas redes sociales online (como Twitter, LinkedIn o Facebook) son creados a través de sugerencias de contactos personalizadas de la plataforma de red social. Los sistemas de recomendación están convirtiendose en un factor importante para influenciar la evolución de la red. Comprender mejor este efecto y aprovechar la oportunidad de obtener más beneficios de la acción de los recomendadores desde una perspectiva amplia de la red son, por tanto, direcciones de investigación que merece la pena investigar, y que estudiamos aquí. Nuestro estudio comprende trabajo teórico y algorítmico, incluyendo la definición y adaptación de métricas de evaluación novedosas. Esto lo complementamos con un exhaustivo trabajo experimental, en el que comparamos múltiples algoritmos de recomendación desarrollados en diferentes áreas, incluyendo la predicción de enlaces, los sistemas de recomendación clásicos y la recuperación de información, junto con otros algoritmos propios del campo de recomendación de contactos. Hemos evaluado los efectos en la evolución de las redes sociales mediante experimentos offline sobre varios grafos de la red social Twitter. Hemos considerado dos tipos de grafos: grafos de interacción entre usuarios (retweets, menciones y respuestas) y grafos de amistad explícitos (relaciones de follow). Con dichos experimentos, se ha medido no sólo el acierto de los recomendadores: también se han estudiado perspectivas más novedosas, como la novedad y diversidad de las recomendaciones, y sus efectos sobre las propiedades estructurales de la red. Finalmente, hemos analizado los efectos de promocionar ciertas métricas globales de diversidad estructural de las recomendaciones sobre el flujo de información que viaja a través de las redes, en términos de la velocidad de la difusión y de la diversidad de la información que reciben los usuarios.Over the last two and a half decades, the development and expansion of recommender systems has progressed increasingly fast. This expansion has given place to the confluence between recommendation technologies and other adjacent areas, notably social networks technologies, which have similarly experienced an exponential growth of their own in the last few years. This thesis explores one of the most novel problems arised from the confluence between both areas: the recommendation of contacts in social networks. Our work focuses, on one hand, on gaining a comprehensive perspective of the effectiveness of a wide range of recommendation algorithms including some of our own original contributions, and considering novel target perspectives beyond the recommendation accuracy. And on the other, on the study of the influence that contact recommendation algorithms have on the evolution of social networks and their properties. A non-negligible fraction of the new links between pairs of users in modern online social networks (such as Twitter, Facebook or LinkedIn) are created through personalized contacts suggestions made by the social network platform. Recommender systems are hence becoming an important factor influencing the evolution of the network. Better understanding this efffect, and taking advantage of the opportunity to draw further benefit from the action of recommenders with a broader network perspective, are therefore a worthwile research direction which we aim to undertake here. Our study comprises algorithmic and theoretical work, including the definition and adaptation of novel evaluation metrics. We complement this with extensive experimental work, where we start by comparing multiple recommendation algorithms developed in different areas including link prediction, classical recommender systems and text information retrieval along with other algorithms from the contact recommendation field. We have evaluated the effects over the evolution of social networks via offline experiments over several graphs extracted from the Twitter social network. Two different types of graphs have been considered: graphs which represent the different interactions between users (retweets, replies and mentions) and explicit graphs (follows relations). With those experiments, we have not only measured the accuracy of the recommendation algorithms, but also more novel perspectives such as the novelty and diversity of the recommendations, and their effects on the structural properties of the network. Finally, we have measured the effects of enhancing the structural diversity of the recommendation over the flow of information which travels through the network in terms of the speed of the diffusion and the diversity of the information received by the different users

Metabuscador con gestión de sesión

Los buscadores son hoy en día la herramienta más ampliamente usada en la Web. Este hecho ha propiciado que las tecnologías, métodos y algoritmos de recuperación de información hayan experimentado una gran desarrollo y una rápida evolución, dando lugar a funcionalidades sofisticadas, así como perspectivas complejas y matizadas sobre la calidad y utilidad de la salida de un buscador. Este desarrollo es imposible sin el desarrollo paralelo de metodologías adecuadas para evaluar y contrastar diferentes soluciones y sistemas, que permitan en definitiva determinar, medir y comparar lo buena que es la respuesta que se proporciona al usuario. Esto ha tenido como consecuencia la concepción de nuevos métodos para realizar la evaluación y comparativa de diferentes sistemas de recuperación de información de información, incluyendo los sistemas de búsqueda en la web. En el presente trabajo se ha desarrollado una aplicación que persigue un doble objetivo. En primer lugar, ofrecer funcionalidades avanzadas de búsqueda sobre un metabuscador que combine resultados de diversos buscadores comerciales. En concreto, se han implementado técnicas y algoritmos de diversificación de resultados, relevance feedback y gestión de sesiones de búsqueda, sobre una agregación de los buscadores Google, Bing, Carrot y Faroo. La diversificación de resultados permite espaciar entre sí los documentos similares en el ranking de resultados. Por otro, las técnicas de relevance feedback, que añaden palabras a una consulta en función de las acciones previas de un usuario en una sesión de búsqueda (un grupo de consultas orientadas a satisfacer una única necesidad de información). Por último, se han desarrollado métodos para realizar la gestión de dichas sesiones a partir de las consultas realizadas por los usuarios, tanto de manera implícita (gestión interna), como de manera explícita, mediante un sistema que permite a los usuarios guardar aquellos documentos que les parezcan más interesantes, así como crear y guardar sesiones de búsqueda. En segundo, se ha implementado en el presente trabajo un sistema que permite la evaluación simultánea de diferentes buscadores web, a partir de métodos de intercalado de resultados. Para alcanzar este objetivo, se ha desarrollado un método probabilístico de intercalado de resultados que permite realizar la evaluación de diversos buscadores a partir de las interacciones de los usuarios con el sistema, sin que el usuario observe ninguna diferencia en el aspecto de la aplicación respecto al uso normal.Search engines are today the most widely and frequently used applications on the Web. That fact has favoured a great development and a fast evolution of information retrieval technologies, methods and algorithms, giving room to sophisticaded functionalities, as well as complex perspectives about the utility and quality of a searcher output. This development is impossible without the parallel development of adequate technologies for evaluating and contrast different solutions and systems, in order to determine, measure and compare how good the provided response to the user is. This has caused the conception of new methods for evaluating and comparing information retrieval systems, including search ones. In this thesis, a web application has been developed, which follows a dual objective. First, offering advance search functionalities over a metasearcher that combines results from several commercial search engines. Specifically, methods and algorithms of result diversification, relevance feedback and management of search sessions have been implemented over an aggregation of the searchers Google, Bing, Carrot and Faroo. Result diversification methods allow the spacing of similar documents from each other within a raking of search results. Furthermore, relevance feedback techniques add words to a given query based on the previous actions of the user in a search session (a group of queries aimed at satisfying a single information need). Finally, methods for managing that sessions have been implemented. Two approaches have been developed: In one hand, an internal session identification and management. On the other hand, an explicit session management, that allows users to store those documents more interesting to them, as well as creating and saving search sessions. Secondly, in the present thesis, a system that allows the simultaneous evaluation of several web search engines, via the interleaving of results has been developed. To achieve this goal, a probabilistic interleaving method that permits the evaluation of multiple search engines by analizing the interactions of the users with the application has been developed, without the user noticing any difference in the application appeareance in relation to the normal use

Personalized Investment Recommendations for Retail Customers

This is a short executive summary of research, development and testing, as well as insights regarding Infinitech Pilot 6. The document aims to provide a high-level overview of what was done, what outcomes were produced and what can be learned moving forward. It is written by the technical team who worked on the pilot at the University of Glasgow (UoG), and is primarilly designed for consumption by staff at the end-user organisation at the National Bank of Greece (NBG)

RELISON: A Framework for Link Recommendation in Social Networks

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON

Recomendación de contactos en redes sociales: modelos algorítmicos, diversidad y evolución de la red

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería informática. Fecha de lectura: 25-05-2021Online social networks like Twitter, Facebook and LinkedIn are used daily by hundreds of millions of people to connect with other users around the world and share information with then. The massive scale of these platforms has led to the development of automated tools to prevent users from being overwhelmed by the vast amount of information they have access to on these sites. Recommender systems are a family of such tools, designed to make individual suggestions of items or people that users might be interested in according to their past personal preferences. This thesis focuses on the study of a particular problem at the con uence of online social networks and recommender systems: the problem of nding users in the network with whom other people wants to connect – the problem known as contact recommendation. We explore this problem from three di erent perspectives. We rst aim to identify the factors leading to the development of e ective contact recommendation approaches, targeting the density of the network. For this, we explore the relation between contact recommendation in social networks and text information retrieval. Considering a collaborative ltering perspective, we explore the utility of adapting search-based models for their use in three di erent aspects of contact recommendation: as standalone recommendation algorithms, as similarity measures, and as samplers and features in learning to rank. Thorough experiments over Twitter and Facebook samples show the e ectiveness of the adapted models in the three roles when compared to the best state of the art approaches. We also explore the potential of contact recommendation algorithms to drive the evolution of social networks towards desirable properties of the network as a whole – beyond aggregating the isolated gains of each user. We investigate the de nition of novel diversity metrics that quantify the e ects of people recommendation over the structure of the network, with a particular focus on notions of structural diversity and weak ties. Over samples from Twitter, we nd that recommending weak ties leads to increased novelty and diversity in the information that reaches the users in the network, with potential implications on the mitigation of lter bubbles. Finally, following up on the evolution perspectives, we address the recommendation task as an interactive cyclic process, where information is constantly exchanged between the users and the system.We develop a simple stochastic approach, based on the classical user-based k nearest neighbors collaborative ltering algorithm, that deals with the uncertainty of the available data for selecting the optimal neighbors of the user we want to generate recommendations for. We explore the utility of this method in dealing with cold start situations over di erent datasets from di erent recommendation domains – including contact recommendation as a particularly compelling one

Contact recommendations in social networks

The increasingly fast development and expansion of recommender systems technology over the last two and a half decades, along with the exponential growth of online social networks in the last few years, has given place to the concurrence of the two areas in several directions. The present chapter focuses on a specific area within this confluence: the recommendation of people to connect with in social networks. We analyze the specifics of contact suggestion as a very particular recommendation task, where both the target users and the target items are people. We give an overview of the most relevant state of the art algorithms in this area, including methods that were originally developed with slightly different problems in mind. We present a global empirical comparison of the reviewed algorithms in order to get a perspective of their comparative performance. We conclude discussing future possible directions for research and development in this area

Beyond accuracy in link prediction

Link prediction has mainly been addressed as an accuracy-targeting problem in social network analysis. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the network where they are applied. Specifically, we consider the structural effects the methods can have if the predicted links are added to the network. We consider further utility dimensions beyond prediction accuracy, namely novelty and diversity. We adapt specific metrics from social network analysis, recommender systems and information retrieval, and we empirically observe the effect of a set of link prediction algorithms over Twitter data

Structural novelty and diversity in link prediction

Link prediction has mainly been addressed as an accuracy-targeting problem in the social networks field. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the social network where they are applied. Specifically, we consider the structural effects the prediction can have if the predicted links are added to the network. We consider further utility dimensions beyond prediction accuracy, namely novelty and diversity. We discuss the adaptation, for this purpose, of specific network, novelty and diversity metrics from social network analysis, recommender systems, and information retrieval