US 12,443,985 B2
Solving sparse data problems in a recommendation system with cold start
Nick Pendar, San Ramon, CA (US); and Arjun Rao, San Ramon, CA (US)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on Dec. 16, 2022, as Appl. No. 18/083,364.
Prior Publication US 2024/0202797 A1, Jun. 20, 2024
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06Q 30/0629 (2013.01); G06Q 30/0641 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
building, by at least one hardware processor of a recommendation system, a generated-item-by-generated-item matrix representing correlations between a plurality of generated items based on observed transactions for the generated items from a generated items transactions database, wherein the generated-item-by-generated-item matrix comprises at least one sparse data scenario for a generated item represented in the matrix;
finding, by the at least one hardware processor, a cold generated item in the generated-item-by-generated-item matrix;
embedding, by the at least one hardware processor, at least a subset of the plurality of generated items into a high-dimensional embedded vector space;
determining, by the at least one hardware processor, a location of the cold generated item in the high-dimensional embedded vector space via one or more attributes of the cold generated item;
finding, in the high-dimensional embedded vector space, a nearest neighbor warm generated item of the cold generated item with a nearest neighbor search technique, comprising: calculating distances between the location of the cold generated item and positions of the subset of the plurality of generated items in the high-dimensional embedded vector space;
identifying the nearest neighbor warm generated item as a suitable proxy for the cold generated item during recommendation processing;
updating, by the at least one hardware processor, the generated-item-by-generated-item matrix to associate the cold generated item with the nearest neighbor warm generated item as its proxy; and
outputting, to an online portal, a recommendation based on the updated generated-item-by-generated-item matrix.