On 12 Apr 2007 at 16:49, karl wettin wrote: > > 12 apr 2007 kl. 12.19 skrev Steffen Heinrich: > > > > > The intended system however can not be trained by user input. The > > suggestions have to come from a given corpus (e.g. an ocasionally > > updated product database). > > Do you think adopting your package to set up the tries from a corpus > > would be fairly easy? > > You can train it with any data you want. So you would need to figure > out what people probably will be searching for. The first thing I can > think of is to extract the most frequenct n grams at a word level in > your title field, or so. It is tough to say what might actually work. > Frequent phrases in the corpus might have nothing to do with consumer > popularity. > > If I understand everyhing, your application is installed locally on > consumer machines. Perhaps you could allow end users to share > anonymous data based on taste and build a set per end user. > Collaborative filtering comes to mind. Reducing a data set to > something relevant usually equals behavioural analysis. > > Hope this helps. > > -- > karl > Wow, this way of data mining is way over my top! I wouldn't know where to begin.
This search is only meant to be used in an ajax-driven web application. And the basic idea is to give the user incentive and turn him to something new, something he didn't think of before. I just generalized on the concept in a mail to Erick under the same subject. There is also a link to a working implementation that served as my model. In the wikipedia article on tries I found the following sentence drawing my attention: "Tries are also well suited for implementing approximate matching algorithms, including those used in spell checking software." Do you have any information about how this can be done? Cheers, Steffen --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
