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From Will Martin <wmartin...@outlook.com>
Subject Re: How to train the model using user clicks when use ltr(learning to rank) module?
Date Fri, 06 Jan 2017 03:56:51 GMT
In the Assemble training data part: the third column indicates the relative
importance or relevance of that doc
Could you please give more info about how to give a score based on what user
clicks?

Hi Jeffery,

Give your questions more detail and there may be more feedback; just a suggestion.
About above,

    some examples of assigning "relative" weighting to training data
    user click info gathered (all assumed but similar to omniture monitoring)
        - position in the result list
        - above/below the fold
        - result page number
    As a information engineer, you might see 2 attributes here: a) user perseverance b) effort
to find the result

    From there, the attributes have a correlation relationship that is not linear and directly
proportional I think:
            easy to find outweighs user perseverance every time because it reduces the need
for such
             extensive perseverance, page #3 for example, doesn't mitigate effort, it drives
effort  towards lower user perseverance need value pairs.
    Ok. That is damn confusing. But its what I would want to do, use the pair in a manner
that reranks a document as if the perseverance and effort were balanced and positioned ...
"relative" to the other training data. What that equation is, will take some more effort....

i'm not sure this response is helpful at all, but i'm going to go with it because I recognize
all of it from AOL, Microsoft and Comcast work. Before the days of ML in Search.

On 1/5/2017 3:33 PM, Jeffery Yuan wrote:

Thanks , Will Martin.

I checked the pdf it's great. but seems not very useful for my question: How
to train the model using user clicks when use ltr(learning to rank) module.

I know the concept after reading these papers. But still not sure how to
code them.




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