Tag Allocation Model: Model Noisy Social Annotations by Reason Finding

We propose the Tag Allocation Model (TAM) to model social annotation data. TAM is a probabilistic generative model, its key feature is finding the latent reason for each tag. A latent reason can be any discrete features of the document (such as words) or a global noise variable. Inferring the reason for each tag helps TAM reduce the ambiguity of a document with multiple tags. By introducing noise as a reason, TAM can handle noise tags naturally. We perform experiments on three real world data sets. The results show that TAM outperforms state-of-the-art approaches in both held-out perplexity and tag recommendation accuracy.