Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
This work addresses robustness issues in recommender systems for users and platforms by mitigating the impact of bots and sparse interactions, though it is incremental as it builds on existing pairwise ranking loss methods.
The paper tackles the problem of training robust large-scale recommender systems from implicit feedback by proposing a sequential learning strategy that filters out abnormal user interactions, such as those from bots or inactive users, to prevent distribution shifts. Experimental results on five large-scale datasets show improvements in ranking measures and computation time compared to state-of-the-art methods.
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.