Abstract: In one embodiment, a method includes preparing an initialized user-embedding matrix, where the user-embedding matrix includes multiple columns corresponding to a user, preparing an initialized concept-embedding matrix, constructing an interaction table representing interactions between entities, where an element (i,j) of the table indicates one or more directional interactions from an entity represented by row i to an entity represented by column j, running a training algorithm for the embeddings in the user-embedding matrix and in the concept-embedding matrix, where the training algorithm trains the embeddings such that embeddings representing entities with interactions are located nearby in the embedding space while embeddings representing entities without interactions are located far apart, and providing the embeddings in the user-embedding matrix and in the concept-embedding matrix to a downstream service.

Abstract: In one embodiment, a method includes accessing a video-content object, determining a first feature vector representing the video-content object using a first recognition module of a first type based on an object in the video-content object, and determining a second feature vector representing the video-content object using a second recognition module of a second type based on the first feature vector. The first type is different from the second type. The method also includes determining a context of the video-content object based on the second feature vector.

Abstract: In one embodiment, a method includes accessing a first feature vector representing a video-content object corresponding to a node in a social graph, wherein the video-content object comprises frames and audio and is associated with text, the first feature vector is based on one or more of the frames; accessing a second feature vector representing the video-content object, wherein the second feature vector is based on at least some of the text; accessing a third feature vector representing the video-content object, wherein the third feature vector is based on one or more portions of the audio; determining a fourth feature vector representing the video-content object, wherein the fourth feature vector is based on a combination of the first, second, and third feature vectors; and determining a context of the video-content object based on the fourth feature vector and social-graph information.

Abstract: In one embodiment, a method includes accessing a first feature vector representing a video-content object corresponding to a node in a social graph, wherein the video-content object comprises frames and audio and is associated with text, the first feature vector is based on one or more of the frames; accessing a second feature vector representing the video-content object, wherein the second feature vector is based on at least some of the text; accessing a third feature vector representing the video-content object, wherein the third feature vector is based on one or more portions of the audio; determining a fourth feature vector representing the video-content object, wherein the fourth feature vector is based on a combination of the first, second, and third feature vectors; and determining a context of the video-content object based on the fourth feature vector and social-graph information.

Abstract: In one embodiment, a method includes accessing for each of a plurality of input images a feature vector corresponding to the input image and metadata indicating a relationship of the input image to a predetermined concept; training with machine learning an image classifier associated with the predetermined concept based on the feature vectors of the input images and the metadata indicating their respective relationships to the predetermined concept; accessing for each of a plurality of evaluation images a feature vector that corresponds to the evaluation image; for each evaluation image calculating with the image classifier as trained a score indicating how closely related the evaluation image is to the predetermined concept, based on the feature vector corresponding to the evaluation image; and providing for display to a user one or more of the evaluation images and their respective scores as calculated by the image classifier.