Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter

Ga Wu, Shivam Khare, Li Zhou, Yael Brumer, Jun-Ping Ng, Ruowei Wang

Abstract: For content recommendation systems on social media platforms, timely, efficient, and accurate estimation of user preferences can effectively improve their performance and enhance the platforms’ activeness. However, efficiency and accuracy are often a pair of trade-offs; Accurate user preference estimation often requires tracking dynamic preference shifting with complex sequential modelling, whereas efficient systems may fail to follow the shift because of a lack of modelling capacity. This paper presents Asynchronous and Asymmetric User Preference Updating System AAUPU, a distributed collaborative filtering system, that can track hundreds of millions of users’ preferences in real time by processing streaming data. The AAUPU system finds a good balance between efficiency and accuracy, making it well-suited for large-scale personalization service needs on social media platforms. We implemented the system on the Google Cloud Platform and successfully tracked user preference for a population of 400 million active Twitter accounts. To evaluate the estimation quality, we conducted massive A/B tests on the two most important service surfaces of Twitter, involving more than ten million users. Our experimental results show that the recommender system based on the AAUPU system significantly improves the overall recommendation performance on the platform.