Recomendación de contactos para la optimización de redes sociales

En el campo del análisis de las redes sociales, dos de los ámbitos más populares son el estudio de la difusión de la información y de la recomendación de contactos. El primero orientado recientemente al marketing viral y el segundo dado al auge que han tenido los sistemas de recomendación en los últimos años y como mejora de la experiencia del usuario en la red. En este Trabajo de Fin de Grado se estudia la aplicación de los algoritmos de maximización de la difusión como recomendación de contactos para optimizar la difusión de información a través de una red social, centrándose en el caso particular de Twitter. Para verificar la eficacia de estos algoritmos, aplicados como recomendación no personalizada, se compararon los resultados contra los obtenidos aplicando métricas de topologías de redes, también aplicados como recomendación no personalizada, y contra algoritmos típicos de recomendación personalizada de contactos. Se analizan los resultados desde tres perspectivas distintas: 1. Los resultados de los propios modelos de maximización de la difusión. 2. Utilización en el marketing viral. 3. Aplicados a la recomendación de contactos. En el primer punto, se prueban los algoritmos clásicos de difusión de información: el modelo de la cascada independiente y el modelo del umbral lineal, con el objetivo de medir cuántos nodos de un grafo pueden ser activados comenzando con un conjunto pequeño de nodos semillas activados inicialmente. En el segundo punto, se prueban los algoritmos de difusión y los de métricas de topologías de redes y se realiza una simulación de marketing viral, donde los nodos semilla seleccionados por cada algoritmo difunden el mismo mensaje cada uno y se mide la velocidad y la propagación en la red. En el tercer punto, aparte de los algoritmos mencionados anteriormente también se prueban unos algoritmos clásicos de recomendación personalizada de contactos. Los usuarios semilla de cada algoritmo son aplicados como recomendación no personalizada al resto de usuarios del grafo, excepto los algoritmos de recomendación personalizada, que para cada usuario recomiendan unos usuarios de manera personalizada. En estas simulaciones múltiples usuarios tienen varios tuits que propagar. Se medirá la velocidad, la cantidad de información nueva y la propagación. Como conclusión, los resultados de la experimentación demuestran que los algoritmos de difusión de información, aplicados como recomendación de contactos no personalizada, pueden ser utilizados para optimizar la información que fluye a través de una red, aunque también tienen sus desventajas frente a otros algoritmos como es el tiempo necesario (NP-hard) para encontrar los usuarios semillas.In the field of social network analysis, two of the most popular areas are the study of the information diffusion and friend recommendation system. The first one focuses on viral marketing recently and the second one to improve the user experience in the network due to the rise of recommendation systems in recent years. In this Bachelor Thesis, we study the use of diffusion maximization algorithms as friend recommendation systems to optimize the information diffusion through a social network, focusing on Twitter. To verify the effectiveness of these algorithms, applied as a non-personalized recommendation, the results are compared with those obtained by applying metrics of network topologies, also applied as a non-personalized recommendation, and against typical personalized recommendation algorithms. The results are analyzed from three different perspectives: 1. The results of the diffusion maximization models. 2. Their use in viral marketing. 3. Applied as friend recommendation systems. Firstly, the classical information diffusion algorithms are tested: the independent cascade model and the linear threshold model, with the objective of measuring how many graph nodes can be activated starting with a small set of activated seed nodes. Secondly, the diffusion algorithms and network topology metrics are tested and a viral marketing simulation is performed, where the seed nodes selected by each algorithm spread the same message and the velocity and propagation are measured. Thirdly, apart from the algorithms mentioned above, we also test classic personalized recommendation algorithms. The seed users of each algorithm will be applied as a non-personalized recommendation to the rest of the users of the graph, except the algorithms of personalized recommendation which for each user will recommend some users in a personalized way. In these simulations, multiple users will have several tweets to propagate. The speed, the amount of new information and the propagation will be measured. In conclusion, the experimentation results demonstrate that information diffusion algorithms applied as non-personalized recommendation systems can be used to optimize information flowing through a network, although they also have their disadvantages compared to other algorithms such as the amount of time needed (NP-hard) to find the seed users

The Metabolism and Growth of Web Forums

We view web forums as virtual living organisms feeding on user's attention and investigate how these organisms grow at the expense of collective attention. We find that the "body mass" ($PV$) and "energy consumption" ($UV$) of the studied forums exhibits the allometric growth property, i.e., $PV_t \sim UV_t ^ \theta$. This implies that within a forum, the network transporting attention flow between threads has a structure invariant of time, despite of the continuously changing of the nodes (threads) and edges (clickstreams). The observed time-invariant topology allows us to explain the dynamics of networks by the behavior of threads. In particular, we describe the clickstream dissipation on threads using the function $D_i \sim T_i ^ \gamma$, in which $T_i$ is the clickstreams to node $i$ and $D_i$ is the clickstream dissipated from $i$. It turns out that $\gamma$, an indicator for dissipation efficiency, is negatively correlated with $\theta$ and $1/\gamma$ sets the lower boundary for $\theta$. Our findings have practical consequences. For example, $\theta$ can be used as a measure of the "stickiness" of forums, because it quantifies the stable ability of forums to convert $UV$ into $PV$, i.e., to remain users "lock-in" the forum. Meanwhile, the correlation between $\gamma$ and $\theta$ provides a convenient method to evaluate the `stickiness" of forums. Finally, we discuss an optimized "body mass" of forums at around $10^5$ that minimizes $\gamma$ and maximizes $\theta$.Comment: 6 figure

Social Transparency through Recommendation Engines and its Challenges: Looking Beyond Privacy

Our knowledge society is quickly becoming a ‘transparent’ one. This transparency is acquired, among other means, by ’personalization’ or ‘profiling’: ICT tools gathering contextualized information about individuals in men–computers interactions. The paper begins with an overview of these ICT tools (behavioral targeting, recommendation engines, ‘personalization’ through social networking). Based on these developments the analysis focus a case study of developments in social network (Facebook) and the trade-offs between ‘personalization’ and privacy constrains. A deeper analysis will reveal unexpected challenges and the need to overcome the privacy paradigm. Finally a draft of possible normative solutions will be depicted, grounded in new forms of individual rights.Recommendation Engines, Profiling, Privacy, ‘Sui Generis’ Copyright

Applying Value Creation Framework to Offer Public Transport Improvement

Public Transportation in urban areas is expected to be main choice for people's mobility. The aim of this research is apply value creation framework based on S-D Logic and Trans Jogja from Yogyakarta and Värmlandstrafik AB Sweden are the case study. This research use direct observation and interview to the related person/ company as the primary data, and to support them use group discussion with the users. This research also use secondary data from some journals, reports, documentations, etc. From the analysis and discussion Värmlandstrafik AB is address the value creation service and opportunity more than Trans Jogja. From conclusion, the need of applying value creation framework in Trans Jogja is to offer public transport improvement as has been illustrated by Trans Jogja. Although the achievement of value creation opportunities are not as high as that achieved by Värmlandstrafik AB, but Trans Jogja should learn about what needs should be improved

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports