'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search
This addresses the challenge of processing vague natural language queries in product search systems, which can reduce cognitive burden for users, but it is incremental as it builds on existing research in interactive search design.
The study tackled the problem of vagueness in natural language queries for product search by analyzing 132 crowd-sourced queries, finding high variance in vagueness levels and identifying user reviews as a potential resource to support vague search intents.
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.