Without offering the 16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry.
This paper tackles the reciprocal recommendation task which has various applications such as online dating, employee recruitment and mentor-mentee matching.
The empirical comparison of the five methods on different recommendation quality criteria shows that no method is overwhelmingly better than the others and that a trade-off need be taken when choosing one for a live system.
However, making that trade-off decision is something that warrants future research, as it is not clear how different criteria affect user experience and likelihood of finding a partner in a live online dating context.
A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores.By detecting communities to which existing users belong and matching new users to these communities, our method is able to recommend existing users who are more likely to reply a date request from new users.Empirical validation using real data from a popular US online dating site reveals that our reciprocal online dating recommendations are significantly better than other traditional methods, achieving 5–100% improvements on average in different evaluation metrics.The five methods are evaluated and compared on a historical data set collected from an online dating website operating in Finland.Additionally, factors influencing the design of online dating recommenders are described, and support for these characteristics are derived from our historical data set and previous research on other data sets.However, recommending dates to new users who have made few interactions with others yet, the so-called “cold start” problem, still poses a problem.