'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search

TL;DR

This paper investigates the vagueness in natural language queries for product search, analyzing user formulations and exploring user reviews as a support source to improve search systems.

Contribution

It introduces a crowd-sourced dataset of natural language information needs and examines the role of user reviews in addressing query vagueness.

Findings

High variance in query vagueness levels

User reviews can support vague search intents

Potential to improve interactive search systems

Abstract

With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able to process these queries because of the vagueness and ambiguity in natural language. Users have adapted their query formulation to what they think the search engine is capable of, which adds to their cognitive burden. With our research, we contribute to the design of interactive search systems by investigating the genuine information need in a product search scenario. In a crowd-sourcing experiment, we collected 132 information needs in natural language. We examine the vagueness of the formulations and their match to retailer-generated content and user-generated product reviews. Our findings reveal high variance on the level of vagueness and the potential of user reviews as a source for supporting users with rather vague search intents.