On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p

Facebook and Youth@SG: Online Privacy and Personal Information Disclosure

Master'sMASTER OF ART

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