WTF: The Who to Follow Service at Twitter by Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, Reza Zadeh.
WTF (“Who to Follow”) is Twitter’s user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which signicantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter’s user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.
You know it is going to be an amusing paper when footnote 1 reads:
The confusion with the more conventional expansion of the acronym is intentional and the butt of many internal jokes. Also, it has not escaped our attention that the name of the service is actually ungrammatical; the pronoun should properly be in the objective case, as in \whom to follow”.
Algorithmic recommendations may miss the mark for an end user.
On the other hand, what about an authoring interface that supplies recommendations of associations and other subjects?
A paper definitely worth a slow read!