Relevance Ranking for Real-Time Tweet Search

Relevance ranking is a key component of many search engines, including the Tweet search engine at Twitter. Users often use Tweet search to discover live discussions and different voices on trending topics or recent events. Tweet search is thus unique due to its focus on real-time content, where both the retrieved content and queries change drastically on an hourly basis. Another important property of Tweet search is that its relevance ranking takes the social endorsements from other users into account, e.g., "likes" and "retweets", which is different from mainly relying on clicks as implicit feedback. The relevance ranking of Tweet search is also subject to strict latency constraints, because every second, a large amount of Tweets are posted and indexed, while tens of thousands of queries are issued to search posted Tweets. Considering the above properties and constraints, we present a relevance ranking system for Tweet search addressing all these challenges at Twitter. We first discuss the formation of the relevance ranking pipeline, which consists of a series of ranking models. We then present the methodology for training the models and the various groups of features we use, including real-time and personalized features. We also investigate approaches of achieving unbiased model training and building up automatic online tuning of system parameters. Experiments using online A/B testing demonstrate the effectiveness of the proposed approaches and we have deployed the proposed relevance ranking system in production for more than three years.