Dynamic content based ranking

We introduce a novel state space model for a set of sequentially time-stamped partial rankings of items and textual descriptions for the items. Based on the data, the model infers text-based themes that are predictive of the rankings enabling forecasting tasks and performing trend analysis. We propose a scaled Gamma process based prior for capturing the underlying dynamics. Based on two challenging and contemporary real data collections, we show the model infers meaningful and useful textual themes as well as performs better than existing related dynamic models.