  In this paper, we propose a theoretically founded sequential strategy for
training large-scale Recommender Systems (RS) over implicit feedback, mainly in
the form of clicks. The proposed approach consists in minimizing pairwise
ranking loss over blocks of consecutive items constituted by a sequence of
non-clicked items followed by a clicked one for each user. We present two
variants of this strategy where model parameters are updated using either the
momentum method or a gradient-based approach. To prevent from updating the
parameters for an abnormally high number of clicks over some targeted items
(mainly due to bots), we introduce an upper and a lower threshold on the number
of updates for each user. These thresholds are estimated over the distribution
of the number of blocks in the training set. The thresholds affect the decision
of RS and imply a shift over the distribution of items that are shown to the
users. Furthermore, we provide a convergence analysis of both algorithms and
demonstrate their practical efficiency over six large-scale collections, both
regarding different ranking measures and computational time.
