Model Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS
Channel: DataTrek
Date: 2022-11-17

#datascience #machinelearning #artificialintelligence #analytics #statistics 

Twitter uses a two-tower model for Accounts Recommendation. The Recommendation system consists of two phases, the retrieval, and the ranking phase. The Retrieval layer has a candidate generator with two towers, one focused on consumption behaviors, the other focused on the production behaviors of the user. The first tower focused on consumption behaviors (what type of content the user consumes), and the other focused on the production behaviors of the user (what type of content the user produce). The idea is that consumption and production behavior may differ, where a person can consume sports, politics, tech, and a wide variety of topics while can produce content on a limited set of topics where their specialization lies. User’s personalized features are added to the consumer tower while the followers-aggregate and producer-related features are incorporated in the producer tower, constituting the candidate generation phase. The retrieval phase is followed by a ranking phase where this set is ranked, in real-time, by a machine learning model to produce the final set of recommendations.
 
Here's a short video for Data Science enthusiasts I have created on the topic covering all the necessary details of the approach, its uniqueness, and the gains seen in the production. Like and subscribe for more such interesting concepts.
 
#datascience #machinelearning #recommendations #twitter #analytics 
 
Blog Link: https://blog.twitter.com/engineering/en_us/topics/insights/2022/model-based-candidate-generation-for-account-recommendations
Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU: https://youtu.be/DN4S96oHRhE
Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search: https://youtu.be/DSQOrBTqmYA
Approximate Nearest Neighbor and Product Quantizer for k-Nearest Neighbor | Embeddings Search: https://youtu.be/50PNumB7s3U


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