  In this paper, we propose a robust sequential learning strategy for training
large-scale Recommender Systems (RS) over implicit feedback mainly in the form
of clicks. Our approach relies on the minimization of a pairwise ranking loss
over blocks of consecutive items constituted by a sequence of non-clicked items
followed by a clicked one for each user. Parameter updates are discarded if for
a given user the number of sequential blocks is below or above some given
thresholds estimated over the distribution of the number of blocks in the
training set. This is to prevent from an abnormal number of clicks over some
targeted items, mainly due to bots; or very few user interactions. Both
scenarios affect the decision of RS and imply a shift over the distribution of
items that are shown to the users. We provide a theoretical analysis showing
that in the case where the ranking loss is convex, the deviation between the
loss with respect to the sequence of weights found by the proposed algorithm
and its minimum is bounded. Furthermore, experimental results on five
large-scale collections demonstrate the efficiency of the proposed algorithm
with respect to the state-of-the-art approaches, both regarding different
ranking measures and computation time.
