https://github.com/undin/recommender-system

https://github.com/undin/recommender-system

Last synced: 8 days ago
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• Host: GitHub
• URL: https://github.com/undin/recommender-system
• Owner: Undin
• Created: 2016-02-16T21:19:17.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-02-17T20:52:52.000Z (over 8 years ago)
• Last Synced: 2024-10-10T18:49:20.163Z (28 days ago)
• Language: Java
• Size: 36.4 MB
• Stars: 0
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Recommender system

* result of recommender system training: https://yadi.sk/d/PvwkEynop2eC9

* sample: https://github.com/Undin/recommender-system/blob/master/src/main/java/com/ifmo/recommendersystem/Main.java

P.S. there aren't some meta-features for `kdd_ipums_la_97-small`, `kdd_ipums_la_98-small`, `kdd_ipums_la_99-small`, `mushroom`, 

`pendigits`, `splice`, `sylva_agnostic`, `sylva_prior` (knn and neural meta-features).

# Files

* `algorithms.json` - list of used feature subset selection algorithms. Contains short name, full class names and options

* `classifiers.json` - list of used classifier algorithms. Contains short name, full class names and options

* `config.json` - config for recommender system builder

* evaluation configs (such as `evaluationConfig.json`, `fastEvaluationConfig.json` and etc.) - configs with parameters to use recommender system

# How to use

* download results and unzip to root of project

* change `DATASETS` to array with paths to your datasets

* run `Main`

* ...

* PROFIT!!! - recommendation will be located in `OUTPUT_DIRECTORY`

# todo

create console util