After the matching procedure eHarmony intends to keep in contact with the (potential) couple, possibly sending out surveys to retrieve a measure of relationship satisfaction and thereby of outcome satisfaction, this would in turn be used to update the matching algorithm with new feedback and improve future results, this means the matching service would “learn” by taking into account the result of previous matches.
eHarmony has a research department, eHarmony Labs. Over the years they have published papers claiming the importance of personality similarity in relationship satisfaction. This is a summary of one of the papers published by eHarmony labs in the Journal of Personality and Social Psychology in 2007 by G. Gonzaga, Belinda Campos & Thomas Bradbury.
Their main results indicate that “similarity and convergence in personality may benefit relationships”. Research supporting the statement that partner similarity is beneficial for the relationship helps eHarmony substantiate their claims as their matching system is based on similarity of personality in the first place as mentioned by Ayres in Super Crunchers.
Relationships benefit from similarity and convergence in the couple’s personality by “promoting similarity and convergence in partners’ shared emotional experiences”
This study uses a sample of college-age couples and newlywed married couples to test their hypothesis that “similarity in partners’ personalities, over and above partners’ personalities considered independently, increases the likelihood that they will have similar emotional experiences” which should promote more “fulfilling relationships”. They test this in three steps namely, they test the claim that partners are similar in personality, then they test the hypothesis that personality and emotional similarity are related to each other and then test whether this is related to relationship quality. Relationship quality was measured using three scales, all based on surveys given to the participants.
Gonzaga and colleagues find support for all three of their hypotheses, meaning that partners were “similar in their personalities and emotions, that personality and emotion similarity were significantly and positively correlated to each other, and that personality and emotion similarity positively correlated with relationship quality”. They also find that these effects are context independent. Next to this, in the two studies they conducted, evidence was found for the fact that similarity in emotion mediated the relationship between personality similarity and relationship satisfaction.
The paper also finds evidence for personality and emotional convergence between partners, meaning they become more alike in those fields over time. Their analysis of the data suggests that “converging and diverging have significant ramifications for the relationship; converging bodes well and diverging bodes poorly”. All in all, this study poses evidence that personality similarity and convergence promote relationship satisfaction, this effect is mediated, amongst others, by emotion similarity and convergence.
As said, this is valuable scientific evidence for eHarmony as their matching system is based on similarity, only users with similar outcomes in the individual satisfaction index are a possible match for each other.
Recommender System for Online Dating Service
In their work, Brozovsky and Petricek (2007) present a recommender system for matchmaking on online dating sites based on collaborative filtering. The recommender algorithm is quantitatively compared to two commonly used global algorithms for online matchmaking on dating sites. Collaborative filtering methods significantly outperform global algorithms that are employed by dating sites. Furthermore, a user experiment was carried out to understand how user perceive different algorithm options.
Recommender systems www.besthookupwebsites.org/cs/flirt4free-recenze have been vastly discussed in literature, however, have found little application in online matchmaking algorithms. The authors state that many online dating web sites have utilized traditional offline matchmaking approaches by agencies, such as questionnaires. While some online dating services, for instance date, match or Perfectmatch, have found success in online matchmaking, their algorithms are inherently simple. As an example, an algorithm may preselect random profiles on conditions, like men of certain age, and users can rate their presented profilesmonly, algorithms of aforementioned web sites are global mean algorithms.