Groupon, Inc.

Bytedance Inc.

Udemy, Inc.

Lawrence Lee Wai

Don Albert Chennavasin

Michael Burton

Devdatta Gangal

Hamish Barney

Daniel Beard

Valampuri Lakshminarayanan

Vijay A Kumar

Amber Roy Chowdhury

Roger Henry Castillo

Bhupesh Bansal

Boris Lerner

Sri Subramaniam

Sridatta Viswanath

Sunil Ramnik Raiyani

Beliz Gokkaya

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.

Method and system for providing electronic marketing communications for a promotion and marketing service

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.

Predictive recommendation system

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.

Predictive recommendation system using tiered feature data

In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion's respective associated relevance score (e.g., a relevance score representing a prediction of the promotion's conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.

Real-time predictive recommendation system using per-set optimization

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

Predictive recommendation system using price boosting

Predictive recommendation system using contextual relevance

Predictive recommendation system using absolute relevance

Systems and related methods of providing promotional offers to consumers are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to provide promotional offers to consumers based on dimensions representing criteria by which promotions may be deemed relevant to a consumer. Some examples of dimensions may include location, time, environment, price, and/or consumer preference. Based on receiving signals from the consumer device, among other sources, indicating associated times, locations, and other characteristics of consumer activity, the apparatus may recognize patterns or trends in consumer behavior, and use such information to predict or influence future consumer behavior.

Systems and methods for providing dimensional promotional offers

Growing community of inventors

Mountain View, CA, United States of America

Lawrence Lee Wai