ah. very nice Diego. Thanks. On 1/6/2017 1:52 PM, Diego Ceccarelli (BLOOMBERG/ LONDON) wrote:
Hi Jeffery, I submitted a patch to the README of the learning to rank example folder, trying to explain better how to produce a training set given a log with interaction data. Patch is available here: https://issues.apache.org/jira/browse/SOLR-9929 And you can see the new version of the README here: https://github.com/bloomberg/lucene-solr/blob/master-ltr/solr/contrib/ltr/example/README.md Please let me know if you have comments or more questions. Cheers Diego From: solr-user@lucene.apache.org<mailto:solr-user@lucene.apache.org> At: 01/06/17 03:57:29 To: solr-user@lucene.apache.org<mailto:solr-user@lucene.apache.org> Subject: Re: How to train the model using user clicks when use ltr(learning to rank) module? 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. -- View this message in context: http://lucene.472066.n3.nabble.com/How-to-train-the-model-using-user-clicks-when-use-ltr-learning-to-rank-module-tp4312462p4312592.html Sent from the Solr - User mailing list archive at Nabble.com.
