Machine learning in facebook

Last week Max Gubin gave a talk on how Facebook exploits machine learning. The talk was more technical then one might had expected, so I share some interesting facts in this posts. I hope it doesn’t disclose any secret information. :)

Max started with a screenshot of the Facebook interface, where each element was highlighted as beneficial of machine learning. Just to name some, they use learning to predict the order of stories in the newsfeed, groups/ads/chat contacts to show you, and even occasions when your account is supposedly hacked, so you need to verify your identity. For most of the tasks learning is probably trivial, but at least two of them involve complicated algorithms (see below).

Facebook engineers face number of difficulties. A user expects to load a page almost instantly, though network infrastructure already imposes some lag. To avoid further delaying, prediction should be done in tens of microseconds. Moreover, half a billion of daily-active users send a lot of queries, so massively-parallel implementations would be too expensive. They have no choice other than sticking to linear models. For example, they train a linear fitness function to rank the stories for the newsfeed (using e.g. hinge loss or logistic loss). It should be trained to satisfy multiple criteria, often contradicting. For example, maximizing personal user experience (showing most interesting stories) might hurt experience of other users (if one has few friends, they are the only users who can read his/her posts) or degrade the system as a whole (showing certain types of news might be not really interesting to anyone, while necessary to improve connectivity of the social network). Those criteria should be balanced in the learning objective, and the coefficients are changing over time. Even the personal user experience cannot be measured easily. The obvious thing to try is to ask users to label interesting stories (or use their Likes). However, such tests are always biased: Facebook tried to use this subjective labelling three times, and all of them were unsuccessful. Users just don’t tell what they really like.

Another challenge is the quickly-changing environment. For example, interest to specific ads may be seasoned. In advertising, one of the strategies is to maximize the click-through rate (CTR). The model for personalized ads should be able to learn online to adapt to changes efficiently. They use probit regression, where online updates can be written in a closed form, unlike to logistic regression (note the linear model again!). It is based on Microsoft’s TrueSkill™ method for learning ranks of players to find good matches and seems similar to what Bing uses for CTR maximization [Graepel et al., 2010].

Finally, Max mentioned the problem of estimating new features. The common practice in the industry is A/B testing, where a group of users is randomly selected to test some feature, and the rest of users are treated as the control group. Then they compare the indicators for those two groups (e.g. average time spent on the website, or clicks made on the newsfeed stories) and apply statistical tests. As usual, samples are typically small. For example, if they want to test a feature for search in Chinese, they take a small group of 10 million users, and hope that some of them will query in Chinese (recall that Facebook is unavailable in China). It is typically hard to prove a statistically significant improvement.

It was partially a hiring event. If you are looking for an internship or a full-time job, you may contact to their HR specialist in Eastern Europe Marina. Facebook also keeps in touch with universities, e.g. invites professors to give talks in their office or develop joint courses. Professors may apply, but I don’t know a contact for that.

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