  In recommender systems, the user-item interaction data is usually sparse and
not sufficient for learning comprehensive user/item representations for
recommendation. To address this problem, we propose a novel dual-bridging
recommendation model (DBRec). DBRec performs latent user/item group discovery
simultaneously with collaborative filtering, and interacts group information
with users/items for bridging similar users/items. Therefore, a user's
preference over an unobserved item, in DBRec, can be bridged by the users
within the same group who have rated the item, or the user-rated items that
share the same group with the unobserved item. In addition, we propose to
jointly learn user-user group (item-item group) hierarchies, so that we can
effectively discover latent groups and learn compact user/item representations.
We jointly integrate collaborative filtering, latent group discovering and
hierarchical modelling into a unified framework, so that all the model
parameters can be learned toward the optimization of the objective function. We
validate the effectiveness of the proposed model with two real datasets, and
demonstrate its advantage over the state-of-the-art recommendation models with
extensive experiments.
