公开(公告)号：US12248966B2

公开(公告)日：2025-03-11

申请号：US17571129

申请日：2022-01-07

Applicant: Bytedance Inc.

Inventor： Don Albert Chennavasin ,  Lawrence Lee Wai ,  Hamish Barney ,  Devdatta Gangal ,  Daniel Beard ,  Valampuri Lakshminarayanan ,  Michael Burton

IPC: G06Q30/0251

Abstract: A computer-executable method, a computer system and a non-transitory computer-readable medium are provided for causing electronic marketing communications of one or more promotions to be generated on a mobile computing device associated with a consumer. A method includes programmatically retrieving promotion data indicative of a plurality of promotions from a computer memory. The method includes determining, using processing circuitry, a promotion score for each of the plurality of promotions. Each promotion score is determined based on consumer profile data, stored consumer activity data, and at least one of: current consumer activity data, current local context data, or predicted consumer activity data. The method further includes outputting indications configured to generate electronic marketing communications associated with the plurality of promotions based on the promotion scores of the plurality of promotions.

公开(公告)号：US12159298B2

公开(公告)日：2024-12-03

申请号：US18156660

申请日：2023-01-19

Applicant: Bytedance Inc.

Inventor： Lawrence Lee Wai

IPC: G06Q30/02 ,  G06Q30/0251 ,  G06Q30/0207

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.