| CPC G06F 16/24578 (2019.01) [G06F 16/258 (2019.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)] | 12 Claims |
|
1. A method for generating a model for recommendations from an item data set for a target data set, the method comprising:
embedding vectorized target data, representative of targets from the target data set, in a latent space using a first embedding function;
embedding a vectorized first set of item data, representative of a first set of items from the item data set, in the latent space using a second embedding function;
selecting at least one target data in the latent space;
identifying, based on proximity to the at least one selected target data in the latent space, a second set of items from the first set of items as candidates for recommendation;
scoring each item in the second set of items using a first scoring mechanism;
ranking each item according to a score for each item;
computing relevance metrics of each ranked item from the second set of items; and
comparing the relevance metrics with a baseline model.
|