Predicting Human Activities from User-Generated Content
Table 10: Per-class accuracy (%) @ keval and ACR scores for the 50-class prediction task. Note that removing h from either fullT or fullA gives the same model. For ACR only, lower is better.
['[ITALIC] keval', '1', '2', '3', '5', '10', '25', '[BOLD] ACR']
While our models are able to make predictions indicating that learning has taken place, it is clear that this prediction task is difficult. The fullA−a,p model performed nearly as well, showing that using only the human-activity relevant content from a user’s history gives similar results to using the full set of content available. When including the attributes and profile for a user, the model typically overfits quickly and generalization deteriorates.