Scalable Personalised Item Ranking through Parametric Density Estimation

TL;DR

This paper introduces a scalable, efficient learning-to-rank method for personalized item recommendation that models positive item densities using exponential family distributions, outperforming traditional pairwise methods.

Contribution

It proposes a novel density estimation approach for ranking, achieving comparable effectiveness to pairwise methods with significantly improved efficiency.

Findings

Outperforms pointwise and pairwise methods in real-world datasets.

Achieves convergence speed similar to pointwise methods.

Demonstrates practical efficiency gains in large-scale applications.

Abstract

Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to the training of an extra model. In this paper, we propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart while performing similarly to the pairwise counterpart in terms of ranking effectiveness. Our approach estimates the probability densities of positive items for each user within a rich class of distributions, viz. \emph{exponential family}. In our formulation, we derive a loss function and the appropriate negative sampling distribution based on maximum likelihood estimation. We also develop a practical technique for risk approximation and a regularisation scheme. We then discuss that our single-model approach is equivalent to an IRGAN variant under a certain condition. Through experiments on real-world datasets, our approach outperforms the pointwise and pairwise counterparts in terms of effectiveness and efficiency.