Patent Application

App. No. 15/908,552

Abstract

Claims (34)

1 . A system, comprising:

a memory configured to store data associated with a matrix having a plurality of entries having data values and a plurality of entries for which there are no data values;

a processor coupled to the memory and configured to:

split the matrix into a plurality of chunks balanced based at least in part on a distribution of entries across the matrix;

send each of the respective chunks to a corresponding worker computer, processor, or thread configured to perform alternative least squares (ALS) processing with respect to the chunk;

receive results from each of the respective worker computers, processors, or threads; and

combine the respective results to determine a predicted value for at least a subset of the missing entries of the matrix.

2 . The system of claim 1 , wherein the processor is configured to split the matrix into a plurality of chunks at least in part by computing a target number of entries per chunk.

3 . The system of claim 2 , wherein the processor is configured to compute the target number of entries per chunk by determining a total number of entries having data values by a number of computers, processors, or threads.

4 . The system of claim 1 , wherein the processor is configured to determine that the matrix is sparsely populated.

5 . The system of claim 4 , wherein the processor is configured to determine that the matrix is sparsely populated by comparing a total number of entries having data values to a size of the matrix.

6 . The system of claim 1 , wherein the processor is configured to split the matrix into a plurality of chunks at least in part by iterative adding columns or rows to a chunk until a next column or row would result in an aggregate number of entries having data values that exceeds a target number of entries.

7 . The system of claim 6 , wherein the processor is further configured to compute column counts and row counts reflecting for each column and row, or portion thereof not yet assigned to a chunk, respectively, a number of entries having data values in that column, row, or portion thereof.

8 . The system of claim 1 , wherein the matrix comprises a sparse set of ratings by each of a plurality of users and wherein the predicted values comprise predicted ratings, and wherein the processor is further configured to use the predicted ratings to determine a recommendation for a user.

9 . A method, comprising:

using a processor to split a matrix having a plurality of entries having data values and a plurality of entries for which there are no data values into a plurality of chunks balanced based at least in part on a distribution of entries across the matrix;

using the processor to send each of the respective chunks to a corresponding worker computer, processor, or thread configured to perform alternative least squares (ALS) processing with respect to the chunk;

receiving at the processor results from each of the respective worker computers, processors, or threads; and

combining the respective results to determine a predicted value for at least a subset of the missing entries of the matrix.

10 . The method of claim 9 , wherein the matrix is split into a plurality of chunks at least in part by computing a target number of entries per chunk.

11 . The method of claim 10 , wherein the target number of entries per chunk is computed at least in part by determining a total number of entries having data values by a number of computers, processors, or threads.

12 . The method of claim 9 , further comprising determining that the matrix is sparsely populated.

13 . The method of claim 12 , wherein the matrix is determined to be sparsely populated by comparing a total number of entries having data values to a size of the matrix.

14 . The method of claim 9 , wherein the matrix is split into a plurality of chunks at least in part by iterative adding columns or rows to a chunk until a next column or row would result in an aggregate number of entries having data values that exceeds a target number of entries.

15 . The method of claim 14 , further comprising computing column counts and row counts reflecting for each column and row, or portion thereof not yet assigned to a chunk, respectively, a number of entries having data values in that column, row, or portion thereof

16 . The method of claim 9 , wherein the matrix comprises a sparse set of ratings by each of a plurality of users and wherein the predicted values comprise predicted ratings, and wherein the predicted ratings are used to determine a recommendation for a user.

17 . A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:

splitting a matrix having a plurality of entries having data values and a plurality of entries for which there are no data values into a plurality of chunks balanced based at least in part on a distribution of entries across the matrix;

sending each of the respective chunks to a corresponding worker computer, processor, or thread configured to perform alternative least squares (ALS) processing with respect to the chunk;

receiving results from each of the respective worker computers, processors, or threads; and

combining the respective results to determine a predicted value for at least a subset of the missing entries of the matrix.

18 . The computer program product of claim 17 , wherein the matrix is split into a plurality of chunks at least in part by computing a target number of entries per chunk.

19 . The computer program product of claim 18 , wherein the target number of entries per chunk is computed at least in part by determining a total number of entries having data values by a number of computers, processors, or threads.

20 . The computer program product of claim 17 , further comprising computer instructions for determining that the matrix is sparsely populated at least in part by comparing a total number of entries having data values to a size of the matrix.

Assignments (14)