<div dir="ltr">Depending of the level of complexity you want in the suggestion is could be very simple to quite hard.<br><br>You could simply use dictionnaries.<br><br>Better is to use semantic sources to find close terms in the semantic space (quite harder I think)<br>
<br>I think that what Yahoo do is a global relevance feedback, ie proposing
terms and queries that close to the one you proposed and already
proposed by others users (collaborative relevance feedback or so).<br><br>You could also make relevance feedback on the user level matching its interests regarding past queries/click (if you store such data).<br><br><br>
<br><div class="gmail_quote">2008/7/21 Jimmy Wales <<a href="mailto:jwales@wikia.com">jwales@wikia.com</a>>:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Linas Vepstas wrote:<br>
> 2008/7/18 Mark (Markie) <<a href="mailto:newsmarkie@googlemail.com">newsmarkie@googlemail.com</a>>:<br>
<div class="Ih2E3d">><br>
>> interested me is the widgets ideas. IMO these are really key things that,<br>
><br>
</div>> Maybe this has been talked to death already, but I started using<br>
> yahoo search recently, and I really like the keyword-refinement<br>
> widget they have -- e.g. if you search for perl, it offers up a bunch<br>
> of perl topics to pick from. I'm guessing that picking from this<br>
> list improves both accuracy and speed.<br>
<br>
Can you send us some links to sample searches to look at this?<br>
<br>
How hard would it be to replicate this functionality in free software?<br>
<div><div></div><div class="Wj3C7c"><br>
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</div></div></blockquote></div><br><br clear="all"><br>-- <br>Gérard Dupont<br>Information Processing Competence Center (IPCC) - EADS DS<br><a href="http://weblab-project.org">http://weblab-project.org</a><br><br>Perception & Machine Learning team - LITIS Laboratory
</div>