  In recent years, group buying has become one popular kind of online shopping
activity, thanks to its larger sales and lower unit price. Unfortunately,
research seldom focuses on recommendations specifically for group buying by
now. Although some recommendation models have been proposed for group
recommendation, they can not be directly used to achieve real-world group
buying recommendation, due to the essential difference between group
recommendation and group buying recommendation. In this paper, we first
formalize the task of group buying recommendations into two sub-tasks. Then,
based on our insights into the correlations and interactions between the two
sub-tasks, we propose a novel recommendation model for group buying, MGBR,
built mainly with a multi-task learning module. To improve recommendation
performance further, we devise some collaborative expert networks and adjusted
gates in the multi-task learning module, to promote the information interaction
between the two sub-tasks. Furthermore, we propose two auxiliary losses
corresponding to the two sub-tasks, to refine the representation learning in
our model. Our extensive experiments not only demonstrate that the augmented
representations in our model result in better performance than previous
recommendation models, but also justify the impacts of the specially designed
components in our model.
