SYSTEM, METHOD AND COMPUTER-READABLE MEDIUM FOR DISTRIBUTION TIME SUGGESTION
The present disclosure relates to a system, a method and a computer-readable medium for distribution time suggestion. The method includes obtaining a first list of viewers in association with a first distributor; obtaining data of the viewers in the first list; obtaining a second list of distributors in association with the viewers in the first list; obtaining data of the distributors in the second list; and determining a distribution time for the first distributor according to the data of the viewers in the first list and the data of the distributors in the second list.
1 . A method for distribution time suggestion, executed by a server, comprising:
obtaining a first list of viewers in association with a first distributor;
obtaining data of the viewers in the first list;
obtaining a second list of distributors in association with the viewers in the first list;
obtaining data of the distributors in the second list; and
determining a distribution time for the first distributor according to the data of the viewers in the first list and the data of the distributors in the second list.
2 . The method according to claim 1 , wherein the data of the viewers in the first list includes contribution predictions of the viewers.
3 . The method according to claim 1 , wherein the data of the distributors in the second list includes similarity scores of the distributors with respect to the first distributor.
4 . The method according to claim 1 , wherein the data of the distributors in the second list includes distribution schedules of the distributors.
5 . The method according to claim 1 , wherein the distribution time is determined to have the least overlap with distribution time of the distributors in the second list having similar attributes with the first distributor.
6 . The method according to claim 2 , wherein the distribution time is determined to have the most overlap with viewing time of the viewers in the first list whose contribution predictions are greater than a contribution threshold.
7 . The method according to claim 2 , wherein the contribution predictions of the viewers are predicted by a machine learning model trained with past contribution data of the viewers in the first list with respect to the first distributor.
8 . The method according to claim 4 , wherein the distribution schedules of the distributors are predicted by a machine learning model trained with past distribution schedules of the distributors in the second list and past viewing schedules of the viewers in the first list.
9 . The method according to claim 1 , wherein the data of the viewers in the first list includes future viewing schedule of the viewers.
10 . The method according to claim 1 , further comprising:
providing, to a user terminal of the first distributor and via a network, a suggestion including the determined distribution time.
11 . A system for distribution time suggestion, comprising one or a plurality of processors, wherein the one or plurality of processors execute a machine-readable instruction to perform:
obtaining a first list of viewers in association with a first distributor;
obtaining data of the viewers in the first list;
obtaining a second list of distributors in association with the viewers in the first list;
obtaining data of the distributors in the second list; and
determining a distribution time for the first distributor according to the data of the viewers in the first list and the data of the distributors in the second list.
12 . A non-transitory computer-readable medium including a program for distribution time suggestion, wherein the program causes one or a plurality of computers to execute:
obtaining a first list of viewers in association with a first distributor;
obtaining data of the viewers in the first list;
obtaining a second list of distributors in association with the viewers in the first list;
obtaining data of the distributors in the second list; and
determining a distribution time for the first distributor according to the data of the viewers in the first list and the data of the distributors in the second list.