Reducing Misinformation in Query Autocompletions

TL;DR

This paper introduces a new method for generating query autocompletions using web crawl anchor texts, reducing reliance on query logs and effectively combating misinformation in search suggestions.

Contribution

It proposes an anchor text-based autocompletion approach that outperforms traditional query log methods for longer queries, enhancing accuracy and trustworthiness.

Findings

Anchor text autocompletions outperform query log autocompletions for queries of 2 or more words.

The approach reduces misinformation in search suggestions.

Query log autocompletions are more effective for very short queries.

Abstract

Query autocompletions help users of search engines to speed up their searches by recommending completions of partially typed queries in a drop down box. These recommended query autocompletions are usually based on large logs of queries that were previously entered by the search engine's users. Therefore, misinformation entered -- either accidentally or purposely to manipulate the search engine -- might end up in the search engine's recommendations, potentially harming organizations, individuals, and groups of people. This paper proposes an alternative approach for generating query autocompletions by extracting anchor texts from a large web crawl, without the need to use query logs. Our evaluation shows that even though query log autocompletions perform better for shorter queries, anchor text autocompletions outperform query log autocompletions for queries of 2 words or more.