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

TL;DR

This paper introduces a robust sequential training strategy for large-scale recommender systems using implicit feedback, incorporating thresholds to mitigate bot influence and providing convergence analysis and empirical validation.

Contribution

It proposes a novel pairwise ranking approach with threshold-based updates and analyzes its convergence, improving robustness and efficiency in large-scale recommendation tasks.

Findings

Effective in reducing bot impact on recommendations

Converges under specified conditions

Outperforms baseline methods on six datasets

Abstract

This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (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 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. They affect the decision of RS by shifting 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 with respect to various ranking measures.