Generating High-Quality Query Suggestion Candidates for Task-Based Search

TL;DR

This paper introduces three novel methods for generating high-quality query suggestion candidates for task-based search without relying on search engine suggestions, demonstrating effectiveness on a custom test collection.

Contribution

The paper proposes three new approaches for query suggestion candidate generation that operate independently of search engines, advancing the state of the art in task-based search.

Findings

Methods generate high-quality suggestions

Effective across multiple information sources

Outperforms existing approaches

Abstract

We address the task of generating query suggestions for task-based search. The current state of the art relies heavily on suggestions provided by a major search engine. In this paper, we solve the task without reliance on search engines. Specifically, we focus on the first step of a two-stage pipeline approach, which is dedicated to the generation of query suggestion candidates. We present three methods for generating candidate suggestions and apply them on multiple information sources. Using a purpose-built test collection, we find that these methods are able to generate high-quality suggestion candidates.