Fitbod, Inc.

Twitter, Inc.

Allen Chen

Jess Venticinque

Arvind Thiagarajan

Tommy Chong

Rohit Jain

Minali Aggarwal

Louis Gutierrez

Charles Edouard Gibbons

Jie Fang

Andrew Hazen Schlaikjer

Yihui Liu

Xiangyi Zheng

An exercise recommendation system determines workout plans for users. The exercise recommendation system receives a profile of a user and a level of variance selected by the user. The profile includes a history of exercises the user has performed, available gym equipment, and exercise goals. The exercise recommendation system inputs the profile to a machine learning model configured to rank a set of exercises for a user to perform. The exercise recommendation system modifies the ranking of exercises based on the level of variance selected by the user. Modification of the ranking is greater for a first level of variance than for a second level of variance less than the first level of variance. The exercise recommendation system generates a workout plan for display within a user interface to the user based on the modified ranking.

Predicting exercises based on user-selected variance

An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.

Determining a user's current exercise capability

According to an aspect, a method for ranking messages of conversation graphs in a messaging platform using machine-learning signals includes receiving a conversation view request to retrieve messages of a conversation graph stored on a messaging platform and determining a plurality of predictive outcomes for each of a plurality of messages of the conversation graph using predictive models. The determining step may include obtaining a plurality of signals relevant to the predictive models, the plurality of signals including data structure-related signals relating to the conversation graph and inputting the plurality of signals to the predictive models. The method includes ranking the plurality of messages based on the predictive outcomes and transmitting at least a subset of the plurality of messages to be rendered on a client application according to the rank.

Ranking messages of conversation graphs in a messaging platform using machine-learning signals

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San Francisco, CA, United States of America

Allen Chen