Hi, from what I see you are using a Neural Network implementation as the model (org.apache.solr.ltr.model.NeuralNetworkModel ?) and I agree is definitely not the best in terms of explainability (org.apache.solr.ltr.model.NeuralNetworkModel#explain).
Effectively it just summarizes the layers, the way the score is calculated is using the weights in the layers and the activation function. To be fair, even with a detailed formula, I suspect, as a human, you wouldn't be getting much more anyway. For the features, it should be easier to explain why they have that value, you should take a look to the way you defined those in the features.json . If hierScore is just a field value and doesn't match, possibly a bug? maybe related with the numerical representation? What is the field type? Cheers -------------------------- *Alessandro Benedetti* Director @ Sease Ltd. *Apache Lucene/Solr Committer* *Apache Solr PMC Member* e-mail: a.benede...@sease.io *Sease* - Information Retrieval Applied Consulting | Training | Open Source Website: Sease.io <http://sease.io/> LinkedIn <https://linkedin.com/company/sease-ltd> | Twitter <https://twitter.com/seaseltd> | Youtube <https://www.youtube.com/channel/UCDx86ZKLYNpI3gzMercM7BQ> | Github <https://github.com/seaseltd> On Fri, 7 Oct 2022 at 12:32, gnandre <arnoldbron...@gmail.com> wrote: > Hi, > > I have implemented LTR (LambdaRank) functionality but there are some search > cases where the relevancy is actually getting worse. I am trying to > understand why some results are ranked over the others. Naturally, I am > using a debug query to understand what is going on. > > e.g. here is the explain response for one of the document: > > doc:en:/help/coder/index.html":"\n0.93952394 = > > (name=model,featureValues=[linkScore=1.7102735,hierScore=3.9314165,originalScore=0.029598212,tfidf_title=-0.3270329,tfidf_body=-0.6185444,tfidf_url=-0.8011434,tfidf_file_name=-0.37964302,tfidf_primary_header_en=-0.32059863,tfidf_secondary_header_en=0.36570454,tfidf_meta_description_en=-0.09497543,tfidf_inlink_text_en=-0.08638504,tfidf_indexed_not_highlighted_en=-0.2544066],layers=[(matrix=75x12,activation=relu),(matrix=1x75,activation=sigmoid)])\n > > Can somebody tell me how the final score of 0.93952394 is getting > calculated for this document? Also, how are the featureValues > calculated? e.g. hierScore field value for this document is actually > 0.5 but it shows up here as 3.9314165. >
