function recommender_prediction_slopeone in Recommender API 6.2

Same name and namespace in other branches

1. 5 recommender.module \recommender_prediction_slopeone()
2. 6 recommender.module \recommender_prediction_slopeone()

This is the implementation of slope-one algorithm, which is supposed to be faster than other algrithms. From the original research paper, the author argues slope-one support incremental update. But this is quite hard to implement. The incremental update doesn't work for now. Missing data is just treated as missing. For slope-one, we don't automatically expand the matrix to have zeros. The responsibility of handling missing data is on the caller functions.

Parameters

$app_name the application name that uses this function.:

$table_name the input table name:

$field_mouse the input table field for mouse:

$field_cheese the input table field for cheese:

$field_weight the input table field weight:

$options an array of options: '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'. 'duplicate': how to handle predictions that already exists in mouse-cheese evaluation. 'preserve' or 'eliminate' 'incremental': whether to 'rebuild' or to 'update'. CAUTION: 'update' doesn't work now.

Return value

null {recommender_prediction} will be filled with prediction data

File

./recommender.module, line 109

Code

function recommender_prediction_slopeone($app_name, $table_name, $field_mouse, $field_cheese, $field_weight, $options = array()) {
}