  Relational probabilistic models have the challenge of aggregation, where one
variable depends on a population of other variables. Consider the problem of
predicting gender from movie ratings; this is challenging because the number of
movies per user and users per movie can vary greatly. Surprisingly, aggregation
is not well understood. In this paper, we show that existing relational models
(implicitly or explicitly) either use simple numerical aggregators that lose
great amounts of information, or correspond to naive Bayes, logistic
regression, or noisy-OR that suffer from overconfidence. We propose new simple
aggregators and simple modifications of existing models that empirically
outperform the existing ones. The intuition we provide on different (existing
or new) models and their shortcomings plus our empirical findings promise to
form the foundation for future representations.
