Retrofitting recommender system for achieving predetermined performance requirements

A computer-implemented method for retrofitting a recommender system to accommodate voluminous data that is organized into records and dimensions comprising:

• a) pre-processing the data by
• i) adding dimensions to the data corresponding to a plurality of clusters over the data, where each record resides in one of the clusters;
• ii) reducing the data and evaluating both a predetermined accuracy threshold and a predetermined performance requirement;
• iii) continuing to reduce the data by a number of records until the predetermined accuracy threshold is met;
• and
• iv) repeatedly removing a highest-frequency non-cluster-corresponding dimension from the data until the predetermined performance requirement is met;
• and,
• b) providing the data as reduced in pre-processing to the recommender system to generate predictions based on a query and the data as reduced in pre-processing.

A computer-implemented method for retrofitting a recommender system to accommodate voluminous data that is organized into records and dimensions comprising:

• a) pre-processing the data by
• i) adding dimensions to the data and a query to be processed, said dimensions corresponding to a plurality of clusters over the data, where each record and said query resides in one of the clusters;
• ii)reducing the data and evaluating both a predetermined accuracy threshold and a predetermined performance requirement;
• iii) repeatedly reducing the data by a number of records until one of the predetermined accuracy threshold and the predetermined performance requirement is first met;
• and
• iv) adding as a dimension to the query a cluster to which the query belongs and,
• b) providing the data as reduced in pre-processing to the recommender system to generate predictions based on a query and the data as reduced in pre-processing.

A computer-implemented method for retrofitting a recommender system to accommodate voluminous data organized into records having dimensions comprising:

• pre-processing the data by reducing the number of records to provide pre-processed data such that both a predetermined accuracy threshold and a predetermined performance requirement are met;
• wherein pre-processing the data comprises repeatedly reducing the data by a number of records until one of the predetermined accuracy threshold and the predetermined performance requirement is first met;
• wherein pre-processing the data further comprises adding dimensions to the data corresponding to a plurality of clusters over the data, where each record resides in one of the clusters;
• providing the pre-processed data to the recommender system to generate predictions based on a query and the pre-processed data as reduced in pre-processing;
• and post-processing the predictions by expanding each dimension of the predictions corresponding to a cluster to a plurality of dimensions distributed in the cluster, and removing each dimension from the predictions that is also found in the query.

A machine-readable medium having instructions stored thereon for execution by a processor to perform a method for retrofitting a recommender system comprising:

• repeatedly reducing data organized into records and dimensions by a number of records until one of a predetermined accuracy threshold and a predetermined performance requirement is first met;
• upon first meeting the predetermined accuracy threshold, repeating removing a highest-frequency dimension from the data until the predetermined performance requirement is also met;
• providing the data wherein at least one of records and dimensions have been removed therefrom to the recommender system to generate predictions also based on a query;
• and,
• adding any dimension previously removed from the data to also meet the predetermined performance requirement to the predictions upon determining that the dimension is mutually exclusive with dimensions of the query.

A machine-readable medium having instructions stored thereon for execution by a processor to perform a method for retrofitting a recommender system comprising:

• repeatedly reducing data organized into records and dimensions by a number of records until one of a predetermined accuracy threshold and a predetermined performance requirement is first met;
• adding dimensions to the data corresponding to a plurality of clusters over the data, where each record resides in one of the clusters;
• upon first meeting the predetermined accuracy threshold, repeatedly removing a highest-frequency non-cluster-corresponding dimension from the data until the predetermined performance requirement is also met;
• providing the data as at least one of records and dimensions have been removed to the recommender system to generate predictions also based on a query;
• adding any dimension previously removed from the data to also meet the predetermined performance requirement to the predictions upon determining that the dimension is mutually exclusive with dimensions of the query;
• and,
• expanding any dimension of the predictions corresponding to a cluster to a plurality of dimensions distributed in the cluster, and removing any dimension from the predictions that is also found in the query.

A machine-readable medium having instructions stored thereon for execution by a processor to perform a method for retrofitting a recommender system comprising:

• a) pre-processing the data by
• i) adding dimensions to the data corresponding to a plurality of clusters over the data, where each record resides in one of the clusters;
• ii)reducing the data and evaluating both a predetermined accuracy threshold and a predetermined performance requirement;
• iii) continuing to reduce the data by a number of records until the predetermined accuracy threshold is met;
• and
• iv) repeatedly removing a highest-frequency non-cluster-corresponding dimension from the data until the predetermined performance requirement is met;
• and,
• b) providing the data as reduced in pre-processing to the recommender system to generate predictions based on a query and the data as reduced in pre-processing.