Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation

TL;DR

This paper introduces a theoretically grounded sequential training strategy for large-scale recommender systems using implicit feedback, employing pairwise ranking over item sequences and thresholds to improve robustness and efficiency.

Contribution

It proposes a novel sequential training approach with thresholding and convergence analysis, enhancing robustness and scalability of recommender systems trained on implicit feedback.

Findings

Effective over six large-scale datasets.

Improves ranking measures and computational efficiency.

Provides convergence guarantees for the proposed algorithms.

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. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent from 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. The thresholds affect the decision of RS and imply a shift over 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, both regarding different ranking measures and computational time.