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View Code? Open in Web Editor NEWA model of a dating website built for machine learning and vector-space experimentation.
A model of a dating website built for machine learning and vector-space experimentation.
/** * @author jeffreymeyerson * * * This experiment tests the results of a data set being put through a * system modeling a dating website. The data set initializes a User set * and defines their UndisclosedPreference set. The User set has been * generated by a Python program which does the following: 1) Define a * Trait set of single-value Traits. The same Trait will have different * values across the population. 2) For each User, for each Trait * defined in the previous step, declare that Trait to have the value of * a random double between 0 and 10. 3) For each User, define an * UndisclosedPreferences which is derived from Traits from the set * created in 1). This Trait set is converted to a single trait through * the TraitConvertible interface, and now belongs to the User's Trait * set wihin a User's UndisclosedPreferences. 4) For each User, define * that user's profile by assigning a random value to each Trait * required by profile definition; this Trait set defines a User. 5) For * each User, initialize that User's list of predicted sought traits * with three things: a) a random Trait t from among that User's * Preference set, b) the value of the Trait belonging to the numerator * in t, and c) the value in the trait belonging to the denominator in * t. * * The goal of the system is to provide relevant suggestions to each * user correlative to that user's UndisclosedPreferences set while * knowing as few of that user's UndisclosedPreferences explicitly as * possible. In terms of a user's UndisclosedPreferences, the system * creates an OptimalMatch vector for each user based on these * Assumptions. It then uses vector similarity to figure out how closely * each other user is to the OptimalMatch, before presenting a set of * Users as a Suggestion. **/
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