Applying the Q-sort Method for Designing Mobile Dating Apps: An Exploration of Matchmaking and Selection Using Personality
Bibliographic record
Abstract
Online dating services have emerged as one of the most popular ways for people to find romantic partners. However, users have complained that they lack important and personal information needed to make a well-informed decision about a person's profile. In this research, we investigate the similarity-attraction hypothesis and propose a personality-oriented matchmaking method for designing dating apps to help users make more informed decisions. Using personality as the primary criterion on dating profiles, we created and evaluated a prototype called Polarity. 54 participants (76% women, 17% men, 8% other; 96% between the ages of 18 and 23) completed a personality inventory and ranked the profiles of people who possess personality traits of their ideal partner using the Q-sort method. Early results suggest that people are more drawn to those who are moderately opposite of themselves than to those who are similar to them, suggesting that attraction may stem from a complimentary appraisal of a person's attributes rather than similarity. The results of this exploratory study shed light on real users' perspectives and hint at the need for more informative profiles and personalized features in dating apps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".