Detecting trends in social bookmarking systems using a probabilistic generative model and smoothing
We propose a method for the detection of trends in social bookmarking systems. Compared to other work in this emerging field, our approach has a more sound statistical basis. In order to cope with the problem of vanishing probabilities due to data sparsity, we apply smoothing and show that it allows for an easy calibration of our trend detector resulting in better generalization and scalability. We test our approach on a collection of 105, 000, 000 bookmarks collected from the del.icio.us bookmarking service. To our knowledge, this is the largest corpus of a real world bookmarking service analyzed in this context. The results show that our method outperforms previously proposed methods and successfully detects trends in the data.