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Sequential models that encode user activity for next action prediction havebecome a popular design choice for building web-scale personalizedrecommendation systems. Traditional methods of sequential recommendation eitherutilize end-to-end learning on realtime user actions, or learn userrepresentations separately in an offline batch-generated manner. This paper (1)presents Pinterest's ranking architecture for Homefeed, our personalizedrecommendation product and the largest engagement surface; (2) proposesTransAct, a sequential model that extracts users' short-term preferences fromtheir realtime activities; (3) describes our hybrid approach to ranking, whichcombines end-to-end sequential modeling via TransAct with batch-generated userembeddings. The hybrid approach allows us to combine the advantages ofresponsiveness from learning directly on realtime user activity with thecost-effectiveness of batch user representations learned over a longer timeperiod. We describe the results of ablation studies, the challenges we facedduring productionization, and the outcome of an online A/B experiment, whichvalidates the effectiveness of our hybrid ranking model. We further demonstratethe effectiveness of TransAct on other surfaces such as contextualrecommendations and search. Our model has been deployed to production inHomefeed, Related Pins, Notifications, and Search at Pinterest.

TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest