Facebook, Inc.

Meta Platforms, Inc.

Daniil Burdakov

Harshdeep Singh

Volodymyr Giginiak

Ittai Sean Barzilay

Laura Ling Javier

Kristina Varshavskaya

Connor C Hayes

Sachin Monga

Lydia Han

Christopher Jon Lang

Aigerim Shorman

Tanner Cotant Christensen

Pradeep George Mathias

Tamar Bar Lev

Deepa Diwakar

Noah Bradley Blecher

Crystal Rhan-Tsor Chen

Karthik Narayan

Shupin Mao

Sunyu Duan

Pedro Veras Bezerra Da Silva

Xiaocong Liang

The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Dynamically ranking media effects based on user and device characteristics

An online system customizes video conversations between users of the online system by providing graphics that are likely to interest the users. The online system may present composite views overlaying selected graphics on a video stream, which may be part of an augmented reality (AR) environment. The graphics include, for example, background graphics, masks, props, visual or particular effects, frames or borders, etc. The online system may use a machine learning model to predict whether a user is likely to select or interact with a particular graphic. The online system can also filter or rank graphics according to user affinities or user connections on the online system. Users may be encouraged to interact with the graphics that are customized to their specific interests, which can promote an engaging video conversation or AR experience.

Video stream customization using graphics

In one embodiment, a method includes accessing a number of candidate notifications stored on the social-networking system. Each of the candidate notifications includes a prompt to perform an action on the social-networking system. The method also includes generating a user feature vector quantifying features of the user; and assessing, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications. The one or more feature vectors includes the user feature vector. The method also includes ranking each of the candidate notifications based at least in part on the respective calculated interaction score; and providing, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user. Each of the provided candidate notifications satisfies a pre-determined threshold value.

Prompt ranking

Growing community of inventors

London, United Kingdom

Daniil Burdakov