Modeling user´s temporal dynamic profile in micro-blogging using clustering method

To help micro-blogging´s users find what they want, we need know what user´s interests are, but user´s interests are not static but change with time, so it is critical to describe what user´s current interests are. The micro-blogging messages published by a user contain the user´s interest information; intuitively, the message that was published recently by a user should have a bigger impact on the user´s current interest than a message that was published long time ago. This paper uses Latent Dirichlet Allocation to extract topics from all messages posted by a user; then, clusters the user´s all messages, and represent user´s interests with the centers of the clusters. When clustering objects, if apply a time-varying influence function on each object, each object has different influence on clustering process; we call this clustering process as clustering method with weight. The experiment shows that the centers of clusters generated by clustering method with weight can depict user´s temporal dynamic profile more accurately than the centers of clusters generated by clustering method without weight.