So, where the ISI is a general relationship satisfaction level, the CSI is specific to one relationship

eHarmony was one of the first companies to introduce an algorithm to match compatible partners on an online dating platform. Many followed their approach of crunching data, such as Match or Perfectmatch. In the case file the performed crunching was a regression model for matchmaking. Even though e-Harmony did not share the underlying algorithm, the business was trusted by people and became profitable only four years after its inception. Today, the algorithm e-Harmony used may very likely be considered outdated or too simplistic. A regression method is not able to capture many of the available data sources nowadays. Social media accounts or other online activity or behavior are an enormous source of information that may find application in an online match making algorithm nowadays. A prime example of a current, more complex algorithm is Tinder. The dating app Tinder performs advanced image processing of user profiles, analyses user behavior within the app and factors in location in real-time, among others. Even though e-Harmony may seem outdated nowadays, it must be noted that the company’s data crunching brought acceptance of such methods to the public and eventually also enabled new comers in the field, such as Tinder, who perform much deeper and detailed analysis of a user’s data.

This led to different studies that investigated whether online dating is as good as offline dating

Next to the ISI, eHarmony computes a ‘couple satisfaction index’ (CSI), this is done using the differences between a couple’s variables, i.e. survey responses. The CSI is estimated using multiple linear regression and is the satisfaction of a given person in a relationship with a specific person. The ISI is then used to cluster people into certain groups with similar ISI levels, from these clusters eHarmony selects the people that have a high CSI level with this person as a ‘match’.

Brozovsky and Petricek compare four algorithms, namely a random algorithm, mean algorithm (also item average algorithm or POP algorithm), and two collaborative filtering methods user-user algorithm and item-item algorithm. The authors test the algorithms on the Libimseti dataset originating from a Czech online dating website ( The dataset consists of 194,439 users and 11,767,448 ratings of profiles. The dataset is noted to be sparser than widely popular dataset from Movielens and Jester with a sparsity of 0.03%. Nonetheless, it is larger in the amount of entries. To benchmark the algorithms three cross-validations measures are employed. Each validation measure uses negative mean square error (NMAE) as a metric. The cross-validations are AllButOne validation, GivenRandomX validation, and production validation. For the AllButOne validation results user-user collaborative filtering algorithm performed the best with mean algorithm performing notably on similar level “due to strong component[s]” in user preference. In the GivenRandomX validation results user-user algorithm achieves again the lowest NMAE. Validation in a production setting did not provide any surprising results. The collaborative filtering algorithms, specifically user-user, outperformed other competitors.

Tinder is a location-based real-time dating application. A major user interface element of Tinder is its swipe feature. Users see pictures of other individuals and with a simple swipe can either like or dislike the other person. If both users like each other they have a “match” and can enter a private chat.

First one to use an algorithmSince e Harmony was the first dating website that based their matches upon an algorithm they did have some scientific impact (Finkel, 2012). After eHarmony other dating websites like Perfectmatch and FindyourFaceMate followed their lead (Finkel, 2012). The users of the sites then thought that they had found a superior way to find the perfect partner. It was found that online dating is good for singles but not better than offline dating and could in certain circumstances even be worse than offline dating. This shows that the eHarmony case did have some influence on other dating services and the way they evolved.

Facebook Dating: Back to basics?In Facebook has released in the United States their own version of a dating app, Facebook Dating. The rationale behind this new venture is that, since you have been feeding data, pictures, updates for years being a Facebook user, the dating app would be able to recommend matches that are presumably more authentic than the standard swiping apps. Moreover, although the app will not match you with any of your Facebook Friends out of obvious reasons, a new feature allows you to add 9 of your friends or Instagram followers on a Secret Crush list. Does this mean we are back to dating people we know in real life? Back to basics? It would indeed add a twist to the contemporaneous online dating arena. However, a journalist reviewing this feature is determined she will never use Secret Crush as “years of app-dating broke my brain and now I’m only capable of being attracted to strangers on the internet”.

Originally, the reasoning behind the Myers-Briggs test stems from Jungian psychology, theories that in the beginning of the 20th century were more in the domain of “hunches” and not empirically tested. Moreover, the namesakes Katherine Briggs and her daughter Isabel Briggs Myers never had a formal training in psychology. Several analyses have shown that the test does not accurately predict career choice, and more than 50 percent of users have different results on a second take, even as the second test is done as early as five weeks later . The test has also been widely discredited by psychologists. CPP, the company that publishes the test has three psychologists on their board, none of whom have used the test in their publications. “It would be questioned by my academic colleagues,” Carl Thoresen, a Stanford psychologist and CPP board member, admitted to the Washington Post in 2012

We were not able to get access to Snow & Carter (2004), therefore we are not able to do an evaluation ourselves.