Numerical Facet Range Partition: Evaluation Metric and Methods
This work addresses a specific gap in faceted navigation for e-commerce search engines, offering incremental improvements in range partitioning.
The paper tackles the problem of optimizing numerical facet range partitions in faceted navigation systems, proposing a new evaluation metric and two algorithms that significantly outperform baseline methods on e-commerce search logs.
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.