Machine learning in online dating

Love and happiness
It can make you do right,
It can make you do wrong
It can make you come home early
It can make you stay out all night long

I went to a LA machine learning meetup last week featuring Jon Morra from eHarmony, where he highlighted some of the uses of machine learning in their online dating platform. I came away impressed by the extent and breadth of machine learning techniques deployed to solve this most human of problems, finding love.

Here is a recording of the presentation:

The central problem

À chaque guenille, son torchon – Québécois proverb*

The central problem of online dating is that there is simply too much choice. To prevent overwhelming users, we want to propose matches intelligently. Abstractly, you want to estimate some “dating compatibility” matrix between different people and serve up some matches up that maximize the summed probability of getting it on.

If the love-distance matrix was small, you had a way to easily compute it, and you’d want to serve up just the one best match to each person, then you could solve this assignment problem with the Hungarian algorithm, for instance. But of course when we’re dealing with millions of users, computing love distances isn’t trivial, and because our matches are imperfect, we’ll want to serve up more than one match. The three-pronged approach outlined by John solves these issues:

  1. Reducing the pool of potential matches using compatibility ratings using self-reported psychological profile surveys, and factors such as sexual preference, age, location, etc.
  2. Computing affinity between potential matches based on demographics, text features, visual features, etc.
  3. Optimally serving matches to users based on affinity, ie. via a daily email


The first part is the most straightforward: based on secret sauce internal surveys and insights from psychology, people are rated as more or less compatible with each other. The compatibility rating encompasses both single-person personality traits and dyad – i.e. similarity – traits.

Results are also filtered by sexual preference, age brackets, location, etc. This first pass eliminates a lot of non-compatible matches based a hard threshold, and thus sparsifies the love-distance matrix to a much more manageable of non-zero elements. I would also venture that it probably results in the creation of cliques, e.g. by location, which allows parallelization for subsequent steps.


The affinity score is a computed probability that two users will communicate, based on a trained logistic regression model. The training data consists of logs of whether two users communicated given their profiles. Training is done using Vowpal Wabbit, a horribly named but potent machine learning package that can do online training of linear and logistic regressions models in the terabyte regime.

You live and die by your features; eHarmony uses classic features like site usage statistics, text features (bag-of-words, I presume), number of photos, etc. extracted from pairs of users. I imagine that the training matrix also includes dyad features like the compatibility rating. Interestingly, eHarmony has also ventured into photo analysis lately.

John first showed examples of using Viola-Jones detectors to extract basic features of images like face area/photo area. The ubiquitous Viola-Jones detector, implemented in OpenCV, uses a cascaded stub classifier to decide whether an image location contains a face. The classifier uses Haar-like features, which can be computed very efficiently using integral images, and is trained using AdaBoost.

Face parts

John then showed more recent results using the Face Parts detector, which I didn’t know about, but is pretty amazing. The idea behind Face Parts is that a face can be deconstructed into parts arranged in a tree structure. Part match – e.g. a score which says how eyebrow-like an image patch is – is determined by the dot product of a template with a histogram of Gaussians (HOG) feature set.


The parts are joined by “springs”, and the total spring deformation determines how energetic a configuration of parts is – low energy configurations are better. A weighted sum of appearance and structural scores determines the “goodness” of a particular configuration.

The goodness of all configurations can be estimated and maximized effectively using a message-passing algorithm because of the special tree structure of the springs model. Several potential tree structures are allowed – for instance, one for front facing faces, another for profile – so that pose estimation, detection, and landmark detection are all done with the same step. Pretty slick.

Training is done in a maximum-margin setting using structured SVM learning methods. Once the model is trained, it’s evaluated on faces in the eHarmony dataset, and various features are extracted from the image: things like ratios of face width to face heights and whether you’re showing cleavage or not. Jon implemented an efficient version which is open source and available on GitHub.

My understanding is that these features are not encoded dyadically in the affinity model: e.g. it doesn’t try to match guys with mustaches with women showing cleavage. Rather, these are monadic features that determine how likely you are to be communicated with, i.e. how attractive you are. And how likely you are to receive communication is important in the next step, matching, which tries to make everybody happy: à chaque guenille son torchon.


Finally, we have to match users optimally. The system sets a goal of 6 to 10 matches per person, and runs a directed flow solver to maximize total flow in a directed acyclic graph – the sum total of affinity scores of matched people – using the CS2 algorithm.

A very interesting cutting edge development – not in production right now – is the idea of serving more or less matches to certain people based on their profile. Some people like more choice, some less – introverts, for example (maybe).

Without knowing a priori if a certain person is a maximizer or satisficer, how do you find their optimal number of matches? One approach would be, for a month, to select a number of matches at random from day to day, and then from then on pick whichever number yielded the most communications for that person. But aren’t we just wasting a lot of days with this strategy?

In fact this problem is the classic multi-armed bandit problem in disguise. You have a series of one-armed bandits – a mathematical idealization of slot machines – which give rewards with certain, unknown, probability. Each trial, you pick a bandit, and gets its reward. The problem is then to maximize the total reward over a given period of time; that is, to minimize the total regret. This requires balancing exploration and exploitation.

One strategy which is not quite optimal but nevertheless very fast and effective is the UCB policy, which says that you should pick whichever arm has the highest upper confidence bound. So the UCB policy could be deployed in this scenario to rapidly find a users’ optimal number of matches.

Here, we have more data that we can exploit – we know users’ profiles. This problem can be treated within the framework of the contextual bandit – basically, classic bandit + regression on features. There’s a very slick paper from Yahoo! labs that shows how to generalize the UCB strategy to the contextual bandit problem, which I highly recommend you check out.


At the end of the day, is it all worth it? John highlighted a paper published in PNAS that showed that people who got married from online dating have higher martial satisfaction than those who met offline, and among dating sites, eHarmony has the best marital satisfaction rates.

Despite the fact that the survey underlying the paper was commissioned by eHarmony itself, the stats look legit, and PNAS is a pretty damn good journal, of course. Of course, one can’t eliminate self-selection biases, i.e. people who want to be in committed relationships select this particular site, as Aziz Ansari points out:

Read up more on applying data science to online dating at the OkCupid blog and in the book Dataclysm.

* for each (female) washcloth, a (male) washcloth, i.e. to each his own. Quebecois French has a lot of terms for washcloth for some reason – see also débarbouillette, which literally translates to “a small item that removes scribbles (from one’s face)”.

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