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However, you can address Lolking through simple multiplication of the clicks on the second and third results and so on.
Modern search engines are actually pretty good at relevance. So LTR is unlikely to be a whole order of magnitude better over relevant search. And there are other means of achieving good results—such as boosting the Looking for an actual ltr or third result if it gets a relatively high number of clicks.
Moreover, LTR and any supervised model Looking for an actual ltr time and resources. LTR is most useful in combination with these other methods. The actual improvement from using LTR will vary wildly with what your data is like, the use case, and how good your ordering already is.
To use LTR, you identify features of your corpus. Using this, you train the model with a given query.
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After that, LTR lets you rerank the results based lte which features were more important given the training sets. Ultimately, like any machine learning technique, LTR is only as good as your model.
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It may also be necessary to do things to normalize the model. For example, if Looking for an actual ltr your website you find that regardless of whatever the first result is, it always gets ten times the clicks as the second result, maybe in your model a second result that gets half as many clicks as the first result ought to be ordered first.
Andrew C. Oliver is manager of technical enablement at Lucidworks, a provider of enterprise search solutions.