➡️ The main driver of this source is the GraphJet in-memory graph processing engine developed by Twitter.
➡️ Estimates what tweets you might find relevant given the users you follow or users with similar interests are engaging with. The Out-of-Network candidates are sourced from two segments: This is where tweet recommendations from users that you do not follow are coming from. ✅ The higher the probability of you interacting with the tweet of a user, the higher the likelihood it will be chosen by the candidate retrieval stage. ➡️ Stale/inactive interaction Edges are periodically deleted. ➡️ The graph is rebuilt each day, so if you started interacting with a user today, you might expect his tweets start coming your way the next day. ➡️ The graph can then be used for multiple use cases. ➡️ By using the previous model, a weight is given to each Edge. ➡️ These historical features are used separately to train a Logistic Regression Model to estimate the probability of a future directed interaction. ➡️ Edges and nodes have features assigned to them. If user A interacted or is following user B, the edge will be directed from A to B and wise versa. ➡️ Interactions between users are represented as Edges of the graph. ➡️ Twitter users are represented by Nodes of the graph.
#Systems like tweetadder Offline
➡️ Apply additional filtering rules defined in the offline part. ➡️ Use Approximate NN index to find the n most similar vectors. ➡️ Once an item comes, we embed it into a vector representation using the model from the offline part.
don’t allow heavy metal songs for children under seven years old). ➡️ Define additional filtering rules for retrieved candidates (e.g. ➡️ Build an Approximate Nearest Neighbours search index from the embeddings. ➡️ We apply previously trained embedding model to all owned inventory items. ➡️ Deploy the model as a service to be later used for real-time embedding. ➡️ We train embedding model that will be used to transform inventory items into vector representations. The procedure in real-world setups usually consists of two steps: It could be a search query on an e-commerce website or a list of Tweets on your Twitter timeline. The goal of a Recommender System is to return a list of recommended items given a certain context.
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