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This paper is an extended version of [Burashnikova et al., 2021, arXiv:2012.06910], where we proposed a theoretically supported sequential strategyfor training a large-scale Recommender System (RS) over implicit feedback,mainly in the form of clicks. The proposed approach consists in minimizingpairwise ranking loss over blocks of consecutive items constituted by asequence of non-clicked items followed by a clicked one for each user. Wepresent two variants of this strategy where model parameters are updated usingeither the momentum method or a gradient-based approach. To prevent updatingthe 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 numberof updates for each user. These thresholds are estimated over the distributionof the number of blocks in the training set. They affect the decision of RS byshifting the distribution of items that are shown to the users. Furthermore, weprovide a convergence analysis of both algorithms and demonstrate theirpractical efficiency over six large-scale collections with respect to variousranking measures.

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)