An Adaptive Match-Making System reflecting the explicit and implicit preferences of users

This is a study of a matchmaking system that adaptively adjusts the recommendation model reflecting the user's implicit preference as well as the explicit one. Many matchmaking systems require their users to assign the level of importance, referred to as weight, of a certain attribute such as age, job, and salary when they select dating partners. However, many users do not know the exact level of importance of each attribute and thus, feel burdened to assign weights. Also, even though users explicitly assign weights, they are often in contrast to the users' actual behaviors in many cases. This paper suggests a new matchmaking system called Adaptive Match-Making System (AMMS) that automatically adjusts the weight of each attribute by analyzing the user's previous behaviors. AMMS provides recommendations for newly entered users on the basis of their explicit-weights assigned by users. However, as the user's behavioral records are accumulated, it begins to build the logistic regression model in order to find out the user's implicit weights and reflects them in proportion to the accuracy of the resulting model. The prototype of AMMS is implemented by using Java and the web editor. It is applied to the created artificial dataset based on the real survey results from major matchmaking companies in Korea.