Cold StartWhen an item or user has just been added to your product and has minimal behavioral or context information.
EmbeddingCompressed numerical representations that encapsulate the underlying features or attributes of the data type.
Model deploymentThe process of creating or replacing a new model that can be accessed through the rank endpoint. Typically performed after a new model is trained.
FeatureAn attribute associated with a user, item, or interaction that is used as input to your model. For example, age could be a user feature, price could be an item feature, device type could be an interaction context feature.
InteractionAn event that relates a user to an item. For example, a user replying to a social post, giving a movie a rating, a user following another user.
ItemAnything that a user interacts with. These are the things you’ll want to rank. For example: social posts, movies or other users.
ModelA machine-learning program that can make predictions from a given input based on patterns of previously seen data.
Online StoreA store for the features that are fed into a machine-learning model. This is a low-latency key-value store that typically contains the user and item context features.
PredictionAn estimation from a model. In our case this is typically a prediction of the most relevant items for each user.
RankingAn optimal ordering of items surfaced to your users. This is what Shaped can find for you.
RecommendationAn optimal ordering of items surfaced to your users. At Shaped we keep this definition the same as ranking.
TrainingWhen a model is training, it is looking at historical data and “learning” the relationships between your users and items. A trained model can be used to predict the items that are most relevant to a user (even if the user or item wasn’t seen at train time).
UserA product user identifier. For example, a logged in user, session, cookie or group identifier.