Density-Ratio Based Personalised Ranking from Implicit Feedback

TL;DR

This paper introduces a novel density-ratio based method for personalized ranking from implicit feedback that combines the efficiency of pointwise approaches with the effectiveness of pairwise ranking, achieving faster convergence and better performance.

Contribution

It proposes a new density-ratio estimation approach that unifies pointwise and pairwise ranking methods for implicit feedback, improving training speed and ranking accuracy.

Findings

Reduces convergence time by one to two orders of magnitude.

Significantly improves ranking performance on real-world datasets.

Demonstrates effectiveness on three different datasets.

Abstract

Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence time owing to the quadratically increasing computational cost with respect to the sample size; it is problematic, particularly for large-scale datasets and complex models such as neural networks. By contrast, a pointwise approach does not directly solve a ranking problem, and is therefore inferior to a pairwise counterpart in top-K ranking tasks; however, it is generally advantageous in regards to the convergence time. This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. The key idea is to estimate the ranking of items in a pointwise manner; we first reformulate the conventional pointwise approach based on density ratio estimation and then incorporate the essence of ranking-oriented approaches (e.g. the pairwise approach) into our formulation. Through experiments on three real-world datasets, we demonstrate that our approach not only dramatically reduces the convergence time (one to two orders of magnitude faster) but also significantly improving the ranking performance.