Search engine result pages (SERPs) can be improved by enhancing 1) the search engine, 2) the available data (i.e. what's free out there for spiders crawling the Web), and 3) user input. We know the impact of GIGO.
Google Suggest is a predictive text entry tool that is designed to improve user input by displaying real-time suggestions as a user types keywords.
Google Suggest serves suggestions that include the inputted string as a prefix. This makes all of its suggestions "narrowing", and excludes phrases that don't start with the prefix the user entered.
If you type "bicycle" in Google Suggest you get:
bicycle parts
bicycle victoria
bicycle trainer
bicycle magazine
bicycle tire
bicycle casino
bicycle trainers
bicycle chain
bicycle retailer
bicycle village
If you enter "bicycle" in the standard Google search box, at the bottom of the search results page you get a completely different set of suggestions.
Searches related to bicycle:
bicycle accessories
bicycle lyrics
cruiser bicycle
history of the bicycle
bicycle review
bicycle games
bicycle safety
schwinn
It is unclear why these two Google features purporting to do the same task - provide related search keywords - generate different results. It seems they are using different algorithms.
To improve Google Suggest, it would have to incorporate expert systems that can analyze the terms a user types and offer knowledge-based suggestions.
An expert system could perform very well, working with detailed knowledge of the field in which the user wants to search.
Before these expert systems are developed, it may be a good idea to use a thesaurus database that can offer satisfying suggestions such as:
bicycle manufacturers
bicycle accessories
mountain bike
racing bike
BMX bike
Tour de France
Maillot Jaune
Floyd Landis
TrekBikes
Cannondale
Raleigh
Bianchi
moped
Google has focused on improving the Google search engine SERPs by creating cutting edge information retrieval algorithms.
Perhaps, to provide an effective and satisfying search experience substantial effort should be directed to designing software for enhancing user input.
Here's my original SETI research
Google Suggest is a predictive text entry tool that is designed to improve user input by displaying real-time suggestions as a user types keywords.
Google Suggest serves suggestions that include the inputted string as a prefix. This makes all of its suggestions "narrowing", and excludes phrases that don't start with the prefix the user entered.
If you type "bicycle" in Google Suggest you get:
bicycle parts
bicycle victoria
bicycle trainer
bicycle magazine
bicycle tire
bicycle casino
bicycle trainers
bicycle chain
bicycle retailer
bicycle village
If you enter "bicycle" in the standard Google search box, at the bottom of the search results page you get a completely different set of suggestions.
Searches related to bicycle:
bicycle accessories
bicycle lyrics
cruiser bicycle
history of the bicycle
bicycle review
bicycle games
bicycle safety
schwinn
It is unclear why these two Google features purporting to do the same task - provide related search keywords - generate different results. It seems they are using different algorithms.
To improve Google Suggest, it would have to incorporate expert systems that can analyze the terms a user types and offer knowledge-based suggestions.
An expert system could perform very well, working with detailed knowledge of the field in which the user wants to search.
Before these expert systems are developed, it may be a good idea to use a thesaurus database that can offer satisfying suggestions such as:
bicycle manufacturers
bicycle accessories
mountain bike
racing bike
BMX bike
Tour de France
Maillot Jaune
Floyd Landis
TrekBikes
Cannondale
Raleigh
Bianchi
moped
Google has focused on improving the Google search engine SERPs by creating cutting edge information retrieval algorithms.
Perhaps, to provide an effective and satisfying search experience substantial effort should be directed to designing software for enhancing user input.
Here's my original SETI research
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