Model-based candidate generation for account recommendations

People come to Twitter with the intention of staying informed on what’s happening within their world of interests. Account recommendations (also known as Who-To-Follow) is a critical piece that helps people connect with accounts relevant to their personalized interests. Behind this product is a two-stage recommendation pipeline, similar to the standard practice for machine learning-driven recommendations in the industry. It begins with candidate generation, where a set of highly relevant candidates are retrieved (in the hundreds to thousands), 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 (single to tens). In the past decade, we have developed dozens of candidate sources to find potentially relevant accounts that match people’s interests. However, each candidate source is typically based on a narrow slice of customer information. It applies algorithms like collaborative filtering or graph expa

People come to Twitter with the intention of staying informed on what’s happening within their world of interests. Account recommendations (also known as Who-To-Follow) is a critical piece that helps people connect with accounts relevant to their personalized interests. Behind this product is a two-stage recommendation pipeline, similar to the standard practice for machine learning-driven recommendations in the industry. It begins with candidate generation, where a set of highly relevant candidates are retrieved (in the hundreds to thousands), followed by a ranking phase where this set is rank