[QUALIFICAÇÃO DE DOUTORADO] User Preferences Dynamics on Evolving Social Networks Learning, Modeling and Prediction
Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. User preferences are fairly dynamic, since users tend to exploit a wide range of items and modify their tastes accordingly over time. Moreover, all the time users are facing with others’ opinions and suffering social influence. In our research, we investigate the interplay of User Preferences and Social Networks over time. We define what are user preferences dynamics and propose a temporal preference model able to describe how user preferences evolve over time through changes on user profiles. As problem solution, we first investigate temporal networks. By modeling a sample of Twitter network as a social temporal network we perceive how nodes evolve in function of centrality metrics and how diferente is the evolution when considering static vs. temporal networks. Then, we explore the idea of centrality-based node event detection in evolving networks. The goal is to detect at what points in time a node change its behavior significantly. Our proposal is a node event mining model with three different strategies for detecting change points. Finally, we join our findings and proposals so far andperformanexperimentalevaluationusingTwitterdatafocusedonourmaingoal: the interplay between user preferences and social networks over time. The discovery is that there is a strong correlation between preference change events and centrality-based node events, specially when considering temporal networks. Moreover, closeness centrality is more suitable when correlating preferences changes and node events than betweenness. From now on our target is to build a complete solution for the preference change prediction problem, taking into account the use of node events detection in continuously evolving networks where the time when edges are active are an explicit element of the representation. Our next steps will concentrate on developing a learning module which will receive signals of events in the network and gather with information about current temporal preferences to make predictions.