Representation Learning via Variational Bayesian Networks

In the recommender system community, this situation is known as the "cold-start" problem [7, 9], where rare ('cold') entities (e.g., We present Variational Bayesian Network (VBN) - a novel Bayesian unpopular items or new items that are introduced to the catalog) are entity representation learning model that utilizes hierarchical and often poorly represented due to insufficient statistics. In the natural relational side information and is particularly useful for modeling language processing community, where the focus is on learning entities in the "long-tail", where the data is scarce. VBN provides representations for words and phrases, a common mitigation is to better modeling for long-tail entities via two complementary mechanisms: increase the training set size by utilizing increasingly larger corpus First, VBN employs informative hierarchical priors that e.g., BERT [20, 39]. However, it was shown that even when enable information propagation between entities sharing common increasing the amount of co-occurrence data, the existence of rare, ancestors. Additionally, VBN models explicit relations between entities out-of-vocabulary entities persists [26, 50, 52, 53].