Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Homophily in online dating: when do you like someone like yourself?
CHI '05 Extended Abstracts on Human Factors in Computing Systems
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Improving Accuracy of Recommender System by Item Clustering
IEICE - Transactions on Information and Systems
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Collaborative user modeling with user-generated tags for social recommender systems
Expert Systems with Applications: An International Journal
Interaction-based collaborative filtering methods for recommendation in online dating
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Machine learning approach for finding business partners and building reciprocal relationships
Expert Systems with Applications: An International Journal
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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.