US 11,057,322 B1
Ranking messages of conversation graphs in a messaging platform using machine-learning signals
Rohit Jain, Seattle, WA (US); Arvind Thiagarajan, Seattle, WA (US); Xiangyi Zheng, San Francisco, CA (US); Minali Aggarwal, Oakland, CA (US); Allen Chen, San Francisco, CA (US); Tommy Chong, South San Francisco, CA (US); and Andrew Hazen Schlaikjer, San Francisco, CA (US)
Assigned to Twitter, Inc., San Francisco, CA (US)
Filed by Twitter, Inc., San Francisco, CA (US)
Filed on Dec. 20, 2019, as Appl. No. 16/723,984.
Int. Cl. G06F 15/16 (2006.01); H04L 12/58 (2006.01); G06N 20/00 (2019.01); G06F 16/901 (2019.01)
CPC H04L 51/046 (2013.01) [G06F 16/9024 (2019.01); G06N 20/00 (2019.01); H04L 51/18 (2013.01); H04L 51/32 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for ranking messages of conversation graphs in a messaging platform using machine-learning signals, the method comprising:
receiving, at a messaging platform, over a network, a conversation view request to retrieve messages of a conversation graph from a user device;
predicting, by the messaging platform, at least one engagement probability for each of a plurality of messages of the conversation graph using at least one predictive model, including:
obtaining a plurality of machine-learning (ML) signals relevant to the at least one predictive model, the plurality of ML signals including data structure-related signals relating to the conversation graph; and
inputting the plurality of ML signals to the at least one predictive model to predict a plurality of predictive outcomes including a negative engagement probability and a positive engagement probability;
computing an engagement value for each of the plurality of messages using the plurality of predictive outcomes;
ranking, by the messaging platform, the plurality of messages based on the engagement values; and
transmitting, by the messaging platform over the network, at least a subset of the plurality of messages to be rendered on a client application according to the rank.