US 11,710,065 B2
Utilizing a bayesian approach and multi-armed bandit algorithms to improve distribution timing of electronic communications
Jun He, Fremont, CA (US); Shiyuan Gu, Santa Clara, CA (US); Zhenyu Yan, Cupertino, CA (US); Wuyang Dai, San Jose, CA (US); Yi-Hong Kuo, Sunnyvale, CA (US); and Abhishek Pani, Sunnyvale, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Apr. 1, 2019, as Appl. No. 16/371,460.
Prior Publication US 2020/0311487 A1, Oct. 1, 2020
Int. Cl. G06F 40/279 (2020.01); G06N 20/00 (2019.01); G06F 18/2415 (2023.01); G06F 18/214 (2023.01); G06N 7/01 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 18/214 (2023.01); G06F 18/24155 (2023.01); G06F 40/279 (2020.01); G06N 7/01 (2023.01)] 20 Claims
OG exemplary drawing
 
1. In a digital medium environment for distributing electronic communications, a computer-implemented method of determining electronic communication send times using a Bayesian approach, the computer-implemented method comprising:
identifying one or more responses to a plurality of electronic communications transmitted to a user;
determining a response rate at a target time granularity via a response rate prediction model by:
identifying, based on responses received from a first attribute group that includes the user, a first label rate indicating a ratio between responses received from, and electronic communications delivered to, the first attribute group over a first coarse time granularity coarser than the target time granularity;
identifying, based on responses received from a second attribute group that includes the user, a second label rate indicating a ratio between responses received from, and electronic communications delivered to, the second attribute group over a second coarse time granularity coarser than the target time granularity and the first coarse time granularity; and
determining the response rate at the target time granularity by applying a user weight to the one or more responses, a first weight to the first label rate, and a second weight to the second label rate;
determining a send time based on the response rate utilizing a Bayes upper-confidence-bound send time model; and
providing an electronic communication to the user based on the send time.