US 11,989,220 B2
System for determining and optimizing for relevance in match-making systems
Fernando Diaz, San Francisco, CA (US); Donald Metzler, Los Angeles, CA (US); and Sihem Amer-Yahia, New York, NY (US)
Assigned to Match Group, LLC, Dallas, TX (US)
Filed by Match Group, LLC, Dallas, TX (US)
Filed on Aug. 12, 2019, as Appl. No. 16/538,090.
Application 16/538,090 is a continuation of application No. 15/231,181, filed on Aug. 8, 2016, granted, now 10,380,158.
Application 15/231,181 is a continuation of application No. 12/829,152, filed on Jul. 1, 2010, granted, now 9,449,282, issued on Sep. 20, 2016.
Prior Publication US 2020/0175047 A1, Jun. 4, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/00 (2019.01); G06F 16/335 (2019.01); G06F 16/9535 (2019.01); G06N 5/048 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)
CPC G06F 16/337 (2019.01) [G06F 16/9535 (2019.01); G06N 5/048 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 15 Claims
OG exemplary drawing
 
1. A method, comprising:
detecting a first behavioral feature for a first potential match for a first entity and a second behavioral feature for a second potential match for a second entity, wherein the first behavioral feature indicates a degree of at least one-way interest in the first entity by a third entity, and the second behavioral feature indicates a degree of at least one-way interest in the second entity by the third entity;
determining a probability of relevance of the first and second potential matches based at least in part upon the first and second behavioral features, wherein the first behavioral feature pertains to a view of a profile of the first entity on a dating service, and the second behavioral feature pertains to a view of a profile of the second entity on the dating service, the profile of the first entity and the profile of the second entity included in a plurality of candidate profiles on the dating service;
training a machine-learned ranking model (i) using a subset of features defined by the candidate profiles only and the probability of relevance of each of the first and second potential matches as inputs, and (ii) by minimizing a total loss, at least in part based on the first entity and the second entity; and
applying the ranking model to rank a potential match for a fourth entity, based at least in part on a first feature vector indicating features of the profile of the first entity and a second feature vector indicating features of the profile of the second entity, wherein the applying is performed at least in part by partitioning a space of feature values into regions.