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

A system for ranking messages of conversation graphs in a messaging platform using machine-learning signals, the system including:

• a conversation graph manager configured to generate a conversation graph based on a reply structure of messages exchanged on a messaging platform, the conversation graph including a data structure of messages of a conversation;
• a timeline manager configured to provide a stream of messages, over a network, in a social timeline of a user on a client application, the timeline manager configured to receive a conversation view request, over the network, from the client application executable by a user device;
• an engagement predictor configured to predict, in response to the conversation view request, a plurality of predictive outcomes for each of a plurality of messages of the conversation graph using at least one predictive model, the plurality of predictive outcomes including a positive engagement probability and a negative engagement probability,
• the engagement predictor configured to obtain a plurality of machine-learning (ML) signals relevant to the at least one predictive model, the engagement predictor configured to input the plurality of ML signals to the at least one predictive model to predict the plurality of predictive outcomes;
• and
• an engagement scorer configured to compute an engagement value for each of the plurality of messages using the predictive outcomes for a respective message,
• the timeline manager configured to rank the plurality of messages using the engagement values, and provide, over the network, at least a subset of the plurality of messages to be rendered on the social timeline according to the rank.

A non-transitory computer-readable medium storing executable instructions that when executed by at least one processor are configured to cause the at least one processor to:

• receive, at a messaging platform, over a network, a conversation view request for messages of a conversation graph from a user device, the conversation graph including a data structure of messages of a conversation;
• select a candidate subset of messages from the conversation graph;
• predict a plurality of predictive outcomes for each message of the candidate subset using at least one predictive model, including:
• obtain a plurality of machine-learning (ML) signals including data structure-related signals relating to the conversation graph;
• and
• input the plurality of ML signals to the at least one predictive model to determine the plurality of predictive outcomes, the plurality of predictive outcomes including a positive engagement probability and a negative engagement probability;
• compute an engagement value for each message of the candidate subset using the predictive outcomes;
• rank the plurality of messages using the engagement values;
• and
• transmit at least a subset of the plurality of messages to be rendered on a client application according to the rank.