(Model Fits across datasets).

Likelihood ratio test compares models 1 and 2 only. Models 1 and 3 could not be compared due to different numbers of observations. Differences in AIC and BIC highlight different model aspects. For both AIC and BIC the lower scores are reflective of better model fit. BIC highlights tags as the driver of prediction in the model. AIC identifies the improved predictive power when using keywords with tags. LRT also identifies this relationship. Note that model fit improves dramatically when applied to data with greater degree of heterogeneity. All of San Francisco (model 4) and all of New York City (model 5) represent greater data heterogeneity than our original pilot study consisting of Chinese restaurants in San Francisco (models 1–3). This is seen when applied to datasets representing the entirety of San Francisco and the entirety of New York City.