  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.
