Adaptive Faceted Search for Product Comparison on the Web of Data

Abstract

In this paper, we study the appropriateness of adaptive faceted search as a search paradigm for e-commerce on the Web of Data. We provide preliminary evidence that the product space in a sample dataset narrows down logarithmically by the number of product features used in a query, and show that the usability of an adaptive, instance-driven faceted search interface is comparable to approaches with hard-wired product features, while improving the depth of product search and comparison.