Numerical Facet Range Partition: Evaluation Metric and Methods

TL;DR

This paper introduces the first formal approach to optimizing numerical facet ranges in search engines, proposing a new evaluation metric and algorithms that improve performance based on real search logs.

Contribution

It formalizes the numerical facet range partition problem, proposes a novel evaluation metric, and develops algorithms that outperform baselines in real-world e-Commerce data.

Findings

Proposed a new evaluation metric for numerical facet ranges.

Developed two algorithms that optimize the metric computationally.

Experimental results show significant performance improvements.

Abstract

Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.