function hook_recommender_info in Recommender API 7.5

extra parameters to consider:

'missing' => how to handle missing data 'none' do nothing 'zero' fill in missing data with zero 'adjusted' skip mice that don't share cheese in common. 'sensitivity' => float. if similarity is smaller enough to be less than a certain value (sensitivity), we just discard those 'lowerbound' => float. if similarity is smaller enough to be less than this value, we just discard those 'duplicate' => how to handle predictions that already exists in mouse-cheese evaluation. 'keep' 'remove' 'incremental' => whether to rebuild the whole similarity matrix, or incrementally update those got changed. 'rebuild', 'refresh'

for slopeone: 'extention' => whether to use 'basic', 'weighted', or 'bipolar' extensions of the algorithm. Please refer to the original research paper. Usually it could just be 'weighted'

Return value

array recommender info array.

1 function implements hook_recommender_info()

Note: this list is generated by pattern matching, so it may include some functions that are not actually implementations of this hook.

rec_example_recommender_info in rec_example/rec_example.module

2 invocations of hook_recommender_info()

recommender_create_record in ./recommender.module

views_handler_filter_recommender::get_value_options in ./recommender.views.inc

Child classes should be used to override this function and set the 'value options', unless 'options callback' is defined as a valid function or static public method to generate these values.

File

./recommender.api.php, line 28

This file documents the APIs provided by the RecommenderAPI module.

Code

function hook_recommender_info() {
  return array(
    'rec_example' => array(
      'title' => t('Recommender API Example on GroupLens data'),
      'algorithm' => 'Item2Item',
      'table' => '<BUILTIN>',
      // this would help Views figure out which base table to join. currently not used.
      'entity_type' => array(
        'similarity' => array(
          'node',
          'node',
        ),
        'prediction' => array(
          'users',
          'node',
        ),
      ),
      //'performance' => 'memory',
      'preference' => 'score',
    ),
    'rec_example_update' => array(
      'title' => t('Recommender API Example on GroupLens data (incremental update)'),
      'algorithm' => 'Item2ItemIncrement',
      'base_app_name' => 'rec_example',
      'table' => '<BUILTIN>',
      // this would help Views figure out which base table to join. currently not used.
      'entity_type' => array(
        'similarity' => array(
          'node',
          'node',
        ),
        'prediction' => array(
          'users',
          'node',
        ),
      ),
      'performance' => 'memory',
      'preference' => 'score',
    ),
  );
}