Usefulness Trumps Fun for Search Autosuggest

20 November
2009

Many thanks to colleague Jake Zarnegar for pointing me toward Slate columnist Michael Agger’s Google Suggest contest.

 

I’m sure you’ve experienced Google Suggest in action: as you type into the search box, Google offers suggestions that change dynamically as you type each letter of your query. The suggestions are sometimes spookily on target but many times flat-out inappropriate.

You’ll find many examples of Google Suggest inappropriateness documented online. But the Slate contest took a different angle, challenging readers to explore the different suggestions made in response to a “less intelligent” Google query versus a “more intelligent” one.” The winners are in:

The winning entry … follows Google Suggest into the realm of moral inquiry. It doesn’t neatly divide into “less intelligent” and “more intelligent,” but it’s the best example I received of how one word can make all the difference. [Is it wrong to…] involves love affairs, God, and younger men. [Is it ethical to…] puts us on the plane of animal research, privacy concerns, and cooking the books.

Putting aside the entertainment and cultural value of Google Suggest, how does it work? Like most things Google, those details are vague:

Our algorithms use a wide range of information to predict the queries users are most likely to want to see. For example, Google Suggest uses data about the overall popularity of various searches to help rank the refinements it offers.

On the Silverchair SCM web content management platform, we also use autosuggest to aid searchers. But there’s no mystery about how it works. Once three characters have been typed into the search box, our search engine starts matching the query against the index of semantic tags that have been applied to that specific content set from our Cortex biomedical taxonomy. Suggestions become more precise with each query character typed, and because we are matching against only those semantic tags applied to the content, the search results set is always targeted and relevant. Our search engine also checks each query against a database of taxonomy equivalents—synonyms, abbreviations, jargon—to normalize the search query and expand it to cover all possible matches.

[caption id="attachment_320" align="aligncenter" width="468"]Silverchair search autosuggest Silverchair search autosuggest[/caption]

Because the content in the products Silverchair builds is tagged so granularly, we can often suggest a more precise term than many searchers start with. Our goals for autosuggest are to save time for users, speed them to the most relevant possible query, and return the most precise answer to their question. Try autosuggest for yourself on the AccessSurgery site we built for McGraw-Hill. Search autosuggest is just one of the many ways a robust taxonomy can promote content discovery.

(I wonder how many of you are running off to play with Google Suggest—a perfect Friday afternoon time-waster…)

[caption id="attachment_324" align="aligncenter" width="468"]Google Suggest Google Suggest[/caption]

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