  Recent years have witnessed extensive researches on developing two tower
recommendation models for relieving information overload. Four building modules
can be identified in such models, namely, user-item encoding, negative
sampling, loss computing and back-propagation updating. To the best of our
knowledge, existing algorithms have researched only on the first three modules,
yet neglecting the backpropagation module. They all adopt a kind of two
backpropagation strategy, which are based on an implicit assumption of equally
treating users and items in the training phase. In this paper, we challenge
such an equal training assumption and propose a novel one backpropagation
updating strategy, which keeps the normal gradient backpropagation for the item
encoding tower, but cuts off the backpropagation for the user encoding tower.
Instead, we propose a moving-aggregation updating strategy to update a user
encoding in each training epoch. Except the proposed backpropagation updating
module, we implement the other three modules with the most straightforward
choices. Experiments on four public datasets validate the effectiveness and
efficiency of our model in terms of improved recommendation performance and
reduced computation overload over the state-of-the-art competitors.
