Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems

Abstract

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. 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 updating the parameters for an abnormally high 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 proof of convergence of the algorithm to the minimizer of the ranking loss, in the case where the latter is convex. Furthermore, experimental results on five large-scale collections demonstrate the efficiency of the proposed algorithm concerning the state-of-the-art approaches, both regarding different ranking measures and computation time.