| CPC H04L 51/04 (2013.01) [G06F 3/04842 (2013.01); G06N 20/00 (2019.01); H04L 51/10 (2013.01); H04L 51/52 (2022.05)] | 20 Claims |
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1. A computer-implemented method comprising:
training a machine learning model to generate relevance scores for a plurality of stickers using as training data, historical data, reflecting prior end-user sticker selections in response to received media content items, wherein the historical data includes attributes and characteristics of previously received media content items and the stickers selected by end-users in reply thereto, including augmentation effects applied to images or videos in the media content items;
subsequent to training the machine learning model:
receiving, by a first device of a first end-user, a media content item communicated by a second device of a second end-user;
generating, at the first device of the first end-user, a reply interface including a set of stickers for use in a reply to the received media content item, by:
using the machine learning model to derive a plurality of relevance scores for a plurality of stickers by analyzing attributes and characteristics of the received media content item, including an indication of an augmentation effect that was applied to an image or video included with the media content item, and using the attributes and characteristics of the received media content item as input to the machine learning model to generate the plurality of relevance scores for the plurality of stickers;
selecting from the plurality of stickers the set of stickers associated with relevance scores that transgress a threshold; and
causing display, by the first device, of the media content item with the reply interface, the reply interface including the set of stickers, each sticker in the set of stickers selectable by the first end-user for sending to the second device, as a reply to the received media content item.
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