Efficient Exploration of Gradient Space for Online Learning to Rank

TL;DR

This paper introduces Null Space Gradient Descent, an efficient exploration method for online learning to rank that accelerates convergence by focusing exploration on promising gradient directions, improving early-stage ranking quality.

Contribution

The paper proposes a novel Null Space Gradient Descent algorithm that reduces exploration to the null space of poor gradients, enhancing learning speed and ranking performance in OL2R.

Findings

Faster convergence compared to state-of-the-art algorithms

Improved early-stage ranking quality

Effective in various public benchmark datasets

Abstract

Online learning to rank (OL2R) optimizes the utility of returned search results based on implicit feedback gathered directly from users. To improve the estimates, OL2R algorithms examine one or more exploratory gradient directions and update the current ranker if a proposed one is preferred by users via an interleaved test. In this paper, we accelerate the online learning process by efficient exploration in the gradient space. Our algorithm, named as Null Space Gradient Descent, reduces the exploration space to only the \emph{null space} of recent poorly performing gradients. This prevents the algorithm from repeatedly exploring directions that have been discouraged by the most recent interactions with users. To improve sensitivity of the resulting interleaved test, we selectively construct candidate rankers to maximize the chance that they can be differentiated by candidate ranking documents in the current query; and we use historically difficult queries to identify the best ranker when tie occurs in comparing the rankers. Extensive experimental comparisons with the state-of-the-art OL2R algorithms on several public benchmarks confirmed the effectiveness of our proposal algorithm, especially in its fast learning convergence and promising ranking quality at an early stage.