How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm | Community Detection | AI
Channel: DataTrek
Date: 2023-04-03

#TwitterRecommendationAlgorithm #SocialGraph #EmbeddingSpaces #SimClusters #MachineLearning #RecommenderSystems #GraphProcessing #NeuralNetworks #ContentRecommendation #Twitter #DataScience #OpenSource #TechNews #Algorithm #ArtificialIntelligence #AI #DataMining #DataAnalysis #datascience #machinelearning #artificialintelligence #analytics #statistics 


Blog Link: https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

In this video, we dive into Twitter's recommendation algorithm and how it works. Twitter's recommendation system suggests a handful of top tweets from the 500 million tweets posted daily, ultimately showing up on your For You timeline.

We take a closer look at the three main stages of the recommendation system: candidate sourcing, ranking using an ML model, and applying heuristics and filters. We explore how Twitter sources its candidates from both in-network and out-of-network sources, and how it uses both a logistic regression model and a real graph model to rank tweets.

We also examine the two approaches Twitter takes to finding relevant tweets outside a user's network: the social graph and embedding spaces. Twitter uses GraphJet, its graph processing engine, to traverse the graph of engagements and follows to estimate relevance, and SimClusters to generate numerical representations of users' and tweets' interests to calculate similarity.

We then delve into the details of Twitter's neural network that ranks the 1500 candidates sourced through the recommendation system, taking into account thousands of features to predict engagement probability.

Finally, we explore the process of blending together tweets with other non-tweet content like ads and follow recommendations to return a personalized timeline to the user's device.

This video is perfect for anyone interested in the behind-the-scenes workings of Twitter's recommendation algorithm, as well as those interested in how recommender systems work in general.

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