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All Projects → DiceTechJobs → RelevancyTuning

DiceTechJobs / RelevancyTuning

Licence: MIT License
Dice.com tutorial on using black box optimization algorithms to do relevancy tuning on your Solr Search Engine Configuration from Simon Hughes Dice.com

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Jupyter Notebook
11667 projects

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diceinformation-retrievalreinforcement-learningsolrluceneoptimization-algorithmssolr-searchblackbox-algorithm

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DiceTechJobs - Relevancy Tuning

Dice.com tutorial on using black box optimization algorithms to tune your Solr search engine configuration, by Simon Hughes ( Dice Data Scientist ). See 'Automated Relevancy Tuning using Black Box Optimization Algorithms.ipynb' for Jupyter Notebook tutorial on using black box optimization algorithms from sci-kit optimize to tune your solr config. Example fake Dice jobs data and a solr core are provided for this example. They are not representative of our real data, nor of our actual Solr search implementation.

Required software

Solr (Solr core is for 5.0+, for earlier solr versions or later ones, you will need to modify the configs and re-index the data from the notebook).

Python 2.7

Python libraries:

  • solrpy
  • pandas
  • numpy
  • scikit-optimize

Slides from my talk

Video of my talk

Related Projects

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