Bilateral Match Prediction Using Collaborative Filtering

The patent introduces an intermediary mechanism that transforms unilateral user opinions into bilateral match predictions. Instead of directly applying collaborative filtering to predict person-to-person compatibility, the system uses user opinions about other users as intermediate data to infer mutual attraction through a mediating algorithm that calculates bidirectional match probabilities.

The patent inverts the traditional collaborative filtering approach by not directly predicting what user A thinks about user B, but rather inferring mutual compatibility by analyzing what both users think about other people. The system works backwards from individual opinions to derive bilateral match potential, reversing the conventional prediction direction.

The patent extracts the personality assessment function from traditional questionnaires and separates it from the matching process. Instead of using comprehensive personality tests, the system extracts only the essential opinion data users naturally provide about other users, discarding the need for complex questionnaire structures while retaining meaningful personality indicators.

The system enables users to self-assess and self-describe through their natural opinions about others, eliminating the need for externally administered questionnaires. Users inadvertently provide personality and preference information through their expressed opinions, which the system automatically processes without requiring structured self-reporting.